Patent Application
20240298964
The present invention relates to the field of digital assessment of diseases. In particular, the present invention relates to computer-implemented methods and systems for quantitatively determining a clinical parameter indicative of the status or progression of a disease. The computer-implemented methods and systems may be used for determining an expanded disability status scale (EDSS) indicative of multiple sclerosis, a forced vital capacity indicative of spinal muscular atrophy, or a total motor score (TMS) indicative of Huntington's disease.
Disease and, in particular, neurological diseases require intensive diagnostic measures for disease management. After the onset of the disease, theses disease, typically, are progressive diseases and need to be evaluated by staging system in order to determine the precise status. Prominent examples among those progressive neurological diseases, there are multiple sclerosis (MS), Huntington's Disease (HD) and spinal muscular atrophy (SMA).
Currently, the staging of such disease requires great efforts and is cumbersome for the patients which need to go to medical specialists in hospitals or doctor's offices. Moreover, staging requires experience at the end of the medical specialist and is often subjective and based on personal experience and judgement. Nevertheless, there are some parameters from disease staging which are particularly useful for the disease management. Moreover, there are other cases such as in SMA were a clinically relevant parameter such as the forced vital capacity needs to be determined by special equipment, i.e. spirometric devices.
For all of these cases, it might be helpful to determine surrogates. Suitable surrogates include biomarkers and, in particular, digitally acquired biomarkers such as performance parameters from tests which am at determining performance parameters of biological functions that can be correlated to the staging systems or that can be surrogate markers for the clinical parameters.
Correlations between the actual clinical parameter of interest, such as a score or other clinical parameter, can be derived from data by various methods.
A first aspect of the present invention provides a computer-implemented method for quantitatively determining a clinical parameter which is indicative of the status or progression of a disease, the computer-implemented method comprising: providing a distal motor test to a user of a mobile device, the mobile device having a touchscreen display, wherein providing the distal motor test to the user of the mobile device comprises: causing the touchscreen display of the mobile device to display a test image; receiving an input from the touchscreen display of the mobile device, the input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together; and extracting digital biomarker feature data from the received input wherein, either: (i) the extracted digital biomarker feature data is the clinical parameter, or (ii) the method further comprises calculating the clinical parameter from the extracted digital biomarker feature data.
A second aspect of the present invention provides a system for quantitatively determining a clinical parameter which is indicative of a the status or progression of a disease, the system including: a mobile device having a touchscreen display, a user input interface, and a first processing unit; and a second processing unit; wherein: the mobile device is configured to provide a distal motor test to a user thereof, wherein providing the distal motor test comprises: the first processing unit causing the touchscreen display of the mobile device to display a test image; the user input interface is configured to receive from the touchscreen display, an input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together; the first processing unit or the second processing unit is configured to extract digital biomarker feature data from the received input.
A third aspect of the present invention provides a computer-implemented method for quantitatively determining a clinical parameter which is indicative of a status or progression of a disease, the computer-implemented method comprising: receiving an input from the mobile device, the input comprising: acceleration data from an accelerometer, the acceleration data comprising a plurality of points, each point corresponding to the acceleration at a respective time; extracting digital biomarker feature data from the received input, wherein extracting the digital biomarker feature data includes: determining, for each of the plurality of points, a ratio of the total magnitude of the acceleration and the magnitude of the z-component of the acceleration at the respective time; and deriving a statistical parameter from the plurality of determined ratios, the statistical parameter including a mean, a standard deviation, a percentile, a median, and a kurtosis.
A fourth aspect of the present invention provides a system for quantitatively determining a clinical parameter which is indicative of a status or progression of a disease, the system including: a mobile device having a an accelerometer, and a first processing unit; and a second processing unit; wherein: the accelerometer is configured to measure acceleration, and either the accelerometer, the first processing unit or the second processing unit is configured to generate acceleration data comprising a plurality of points, each point corresponding to the acceleration at a respective time; the first processing unit or the second processing unit is configured to extract digital biomarker feature data from the received input by: determining, for each of the plurality of points, a ratio of the total magnitude of the acceleration and the magnitude of the z-component of the acceleration at the respective time; and deriving a statistical parameter from the plurality of determined ratios, the statistical parameter including a mean, a standard deviation, a percentile, a median, and a kurtosis.
As used in the following, the terms “have”, “comprise” or “include” or any arbitrary grammatical variations thereof are used in a non-exclusive way. Thus, these terms may both refer to a situation in which, besides the feature introduced by these terms, no further features are present in the entity described in this context and to a situation in which one or more further features are present. As an example, the expressions “A has B”, “A comprises B” and “A includes B” may both refer to a situation in which, besides B, no other element is present in A (i.e. a situation in which A solely and exclusively consists of B) and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D or even further elements.
Further, it shall be noted that the terms “at least one”, “one or more” or similar expressions indicating that a feature or element may be present once or more than once typically will be used only once when introducing the respective feature or element. In the following, in most cases, when referring to the respective feature or element, the expressions “at least one” or “one or more” will not be repeated, non-withstanding the fact that the respective feature or element may be present once or more than once.
Further, as used in the following, the terms “preferably”, “more preferably”, “particularly”, “more particularly”, “specifically”, “more specifically” or similar terms are used in conjunction with optional features, without restricting alternative possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The invention may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by “in an embodiment of the invention” or similar expressions are intended to be optional features, without any restriction regarding alternative embodiments of the invention, without any restrictions regarding the scope of the invention and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non-optional features of the invention.
Embodiment 1: A computer-implemented method for quantitatively determining a clinical parameter which is indicative of the status or progression of a disease, the computer-implemented method comprising:
Embodiment 2: A computer-implemented method according to embodiment 1, wherein:
Embodiment 3: A computer-implemented method according to embodiment 1, wherein:
Embodiment 4: A computer-implemented method according to any one of embodiments 1 to 3, wherein:
Embodiment 5: A computer-implemented method according to any one of embodiments 1 to 3, further comprising:
Embodiment 6: The computer-implemented method of any one of embodiments 1 to 5, wherein:
Embodiment 7: The computer-implemented method of embodiment 6, wherein:
Embodiment 8: The computer-implemented method of any one of embodiments 1 to 7, wherein:
Embodiment 9: The computer-implemented method of embodiment 8, wherein:
Embodiment 10: The computer-implemented method of embodiment 8 or embodiment 9, wherein:
Embodiment 11: The computer-implemented method of any one of embodiments 1 to 10, wherein:
Embodiment 12: The computer-implemented method of embodiment 11, wherein:
Embodiment 13: The computer-implemented method of any one of embodiments 1 to 12, wherein:
Embodiment 14: The computer-implemented method of embodiment 13, wherein:
Embodiment 15: The computer-implemented method of any one of embodiments 1 to 14, wherein:
Embodiment 16: The computer implemented method of embodiment 15, wherein:
The purpose of the present invention is to use a simple mobile device-based test to determine progress of a disease which affects a user's motor control. In view of that, the success of a test preferably depends on the extent to which a user is successfully able to bring the first point and the second point together without lifting their fingers from the touchscreen display surface. The step of determining whether an attempt has been successful preferably includes determining a distance between the location where the first finger leaves the touchscreen display and the location where the second finger leaves the touchscreen display. A successful attempt may be defined as an attempt in which this distance falls below a predetermined threshold. Alternatively, the step of determining whether an attempt has been successful may include determining a distance from a midpoint between the initial location of the first point and an initial location of the second point, of the location where the first finger leaves the touchscreen display, and a distance from a midpoint between the initial location of the first point and an initial location of the second point, of the location where the second finger leaves the touchscreen display. A successful attempt may be defined as an attempt where the average of the two distances is below a predetermined threshold or alternatively, an attempt where both of the distances are below a predetermined threshold.
Embodiment 17: The computer-implemented method of any one of embodiments 1 to 14, wherein:
Embodiment 18: The computer-implemented method of any one of embodiments 15 to 17, wherein:
Embodiment 19: The computer-implemented method of embodiment 18, wherein:
The percentile may be the 5%, 10%, 15%, 20%, 25%, 30%, 33%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 66%, 67%, 70%, 75%, 80%, 85%, 90%, 95%.
Embodiment 20: The computer-implemented method of any one of embodiments 14 to 19, wherein:
Embodiment 21: The computer-implemented method of embodiment 20, wherein:
Embodiment 22: The computer-implemented method of any one of embodiments 15 to 21, wherein:
Embodiment 23: The computer-implemented method of any one of embodiments 15 to 22, wherein:
Embodiment 24: The computer-implemented method of any one of embodiments 15 to 23, wherein:
Embodiment 25: The computer-implemented method of any one of embodiments 1 to 24, wherein:
Embodiment 26: The computer-implemented method of embodiment 25, wherein:
Embodiment 27: The computer-implemented method of embodiment 20, wherein:
Embodiment 28: The computer-implemented method of any one of embodiments 25 to 27, wherein:
Embodiment 29: The computer-implemented method of any one of embodiments 25 to 28, wherein:
Embodiment 30: The computer-implemented method of any one of embodiments 25 to 29, wherein:
Embodiment 31: The computer-implemented method of any one of embodiments 25 to 30, wherein:
Embodiment 32: The computer-implemented method of any one of embodiments 1 to 31, further comprising:
Embodiment 33: The computer-implemented method of embodiment 32, wherein:
Embodiment 34: The computer-implemented method of embodiment 33, wherein:
Embodiment 35: The computer implemented method of 33, wherein:
Embodiment 36: The computer-implemented method of any one of embodiments 1 to 35, wherein:
Embodiment 37: The computer-implemented method of any one of embodiments 1 to 36, wherein:
Embodiment 38: The computer-implemented method of embodiment 37, wherein:
Embodiment 39: A system for quantitatively determining a clinical parameter which is indicative of a the status or progression of a disease, the system including:
Embodiment 40: The system of embodiment 39, wherein:
Embodiment 41: The system of embodiment 39, wherein:
Embodiment 42: The system of any one of embodiments 39 to 41, wherein:
Embodiment 43: The system of any one of embodiments 39 to 41, wherein:
Embodiment 44: The system of any one of embodiments 39 to 43, wherein:
Embodiment 45: The system of embodiment 44, wherein:
Embodiment 46: The system of any one of embodiments 39 to 45, wherein:
Embodiment 47: The system of embodiment 46, wherein:
Embodiment 48: The system of embodiment 46 or embodiment 47, wherein:
Embodiment 49: The system of any one of embodiments 39 to 48, wherein:
Embodiment 50: The system of embodiment 49, wherein:
Embodiment 51: The system of any one of embodiments 39 to 50, wherein:
Embodiment 52: The system of embodiment 51, wherein:
Embodiment 53: The system of any one of embodiments 39 to 52, wherein:
Embodiment 54: The system of embodiment 53, wherein:
Embodiment 55: The system of any one of embodiments 39 to 52, wherein:
Embodiment 56: The system of any one of embodiments 53 to 55, wherein:
Embodiment 57: The system of embodiment 56, wherein:
Embodiment 58: The system of any one of embodiments 53 to 57, wherein:
Embodiment 59: The system of embodiment 58, wherein:
Embodiment 60: The system of any one of embodiments 53 to 59, wherein:
Embodiment 61: The system of any one of embodiments 53 to 60, wherein:
Embodiment 62: The system of any one of embodiments 53 to 61, wherein:
Embodiment 63: The system of any one of embodiments 39 to 62, wherein:
Embodiment 64: The system of embodiment 63, wherein:
Embodiment 65: The system of embodiment 64, wherein:
Embodiment 66: The system of any one of embodiments 63 to 65, wherein:
Embodiment 67: The system of any one of embodiments 63 to 66, wherein:
Embodiment 68: The system of any one of embodiments 63 to 67, wherein:
Embodiment 69: The system of any one of embodiments 63 to 68, wherein:
Embodiment 70: The system of any one of embodiments 39 to 69, wherein:
Embodiment 71: The system of embodiment 70, wherein:
Embodiment 72: The system of 71, wherein:
Embodiment 73: The system of embodiment 71, wherein:
Embodiment 74: The system of any one of embodiments 39 to 73, wherein:
Embodiment 75: The system of any one of embodiments 39 to 74, wherein:
Embodiment 76: The system of any one of embodiments 39 to 74, wherein:
Embodiment 77: The system of any one of embodiments 39 to 76, further comprising a machine learning system for determining the at least one analysis model for predicting the clinical parameter indicative of a disease status, the machine learning system comprising:
Embodiment 78: A computer-implemented method for quantitatively determining a clinical parameter which is indicative of a status or progression of a disease, the computer-implemented method comprising:
Embodiment 79: A system for quantitatively determining a clinical parameter which is indicative of a status or progression of a disease, the system including:
Embodiment 80: A computer-implemented method for quantitatively determining a clinical parameter indicative of a status or progression of a disease, the computer-implemented method comprising:
Embodiment 81: A computer-implemented method according to embodiment 80, wherein: the extracted digital biomarker feature data is the clinical parameter.
Embodiment 82: A computer-implemented method according to embodiment 80, further comprising:
Embodiment 83: The computer-implemented method of any one of embodiments 80 to 82, wherein:
Embodiment 84: The computer-implemented method of embodiment 83, wherein:
Embodiment 85: The computer-implemented method of any one of embodiments 80 to 82, wherein:
Embodiment 86: The computer-implemented method of embodiment 85, wherein:
Embodiment 87: The computer-implemented method of any one of embodiments 80 to 86, wherein:
Embodiment 88: The computer-implemented method of embodiment 87, wherein:
Embodiment 89: The computer-implemented method of embodiment 88, wherein:
Embodiment 90: The computer-implemented method of any one of embodiments 87 to 89, wherein:
Embodiment 91: The computer-implemented method of any one of embodiments 87 to 90, wherein:
Embodiment 92: The computer-implemented method of any one of embodiments 80 to 91, further comprising the steps of:
Embodiment 93: The computer-implemented method of embodiment 92, wherein:
Embodiment 94: The computer-implemented method of embodiment 93, wherein:
Embodiment 95: The computer implemented method of embodiment 93, wherein:
Embodiment 96: The computer-implemented method of any one of embodiments 80 to 95, wherein:
Embodiment 97: The computer-implemented method of any one of embodiments 80 to 96, wherein:
Embodiment 98: The computer-implemented method of embodiment 97, wherein:
Embodiment 99: A system for quantitatively determining a clinical parameter indicative of a status or progression of a disease, the system including:
Embodiment 100: The system of embodiment 99, wherein:
Embodiment 101: The system of embodiment 99, wherein:
Embodiment 102: The system of any one of embodiments 99 to 101, wherein:
Embodiment 103: The system of embodiment 102, wherein:
Embodiment 104: The system of embodiment any one of embodiments 99 to 101, wherein:
Embodiment 105: The system of embodiment 104, wherein:
Embodiment 106: The system of any one of embodiments 99 to 105, wherein:
Embodiment 107: The system of embodiment 106, wherein:
Embodiment 108: The system of embodiment 107, wherein:
Embodiment 109: The system of any one of embodiments 106 to 108, wherein:
Embodiment 110: The system of any one of embodiments 106 to 109, wherein:
Embodiment 111: The system of any one of embodiments 99 to 110, wherein:
Embodiment 112: The system of embodiment 111, wherein:
Embodiment 113: The system of embodiment 112, wherein:
Embodiment 114: The system of embodiment 112, wherein:
Embodiment 115: The system of any one of embodiments 99 to 114, wherein:
Embodiment 116: The system of any one of embodiments 99 to 115, wherein:
Embodiment 117: The system of any one of embodiments 99 to 115, wherein:
Embodiment 118: The system of any one of embodiments 99 to 117, further comprising a machine learning system for determining the at least one analysis model for predicting the at least one clinical parameter indicative of a disease status, the machine learning system comprising:
Embodiment 119: A computer-implemented method comprising one, two, or all of:
Embodiment 120: A system comprising one, two, or all of:
The above disclosure relates primarily to the determination of a clinical parameter which is indicative of a status or progression of a disease. However, in some cases, the invention may provide a computer-implemented method of determining a status or progression of a disease, the computer-implemented method comprising: providing a distal motor test to a user of a mobile device, the mobile device having a touchscreen display, wherein providing the distal motor test to the user of the mobile device comprises: causing the touchscreen display of the mobile device to display a test image; receiving an input from the touchscreen display of the mobile device, the input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together; and extracting digital biomarker feature data from the received input wherein, either: (i) the extracted digital biomarker feature data is the clinical parameter, or (ii) the method further comprises calculating the clinical parameter from the extracted digital biomarker feature data; and determining the status or progression of the disease based on the determined clinical parameter.
Equivalently, a further aspect of the invention provides a system for determining the status or progression of a disease, the system including: a mobile device having a touchscreen display, a user input interface, and a first processing unit; and a second processing unit; wherein: the mobile device is configured to provide a distal motor test to a user thereof, wherein providing the distal motor test comprises: the first processing unit causing the touchscreen display of the mobile device to display a test image; the user input interface is configured to receive from the touchscreen display, an input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together; the first processing unit or the second processing unit is configured to extract digital biomarker feature data from the received input, and to determine a clinical parameter based on the extracted digital biomarker feature data; and the first processing unit or the second processing unit is configured to determine the status or progression of the disease based on the determined clinical parameter.
A further aspect of the invention may provide a computer-implemented method for determining a status or progression of a disease, the computer-implemented method comprising: receiving an input from the mobile device, the input comprising: acceleration data from an accelerometer, the acceleration data comprising a plurality of points, each point corresponding to the acceleration at a respective time; extracting digital biomarker feature data from the received input, wherein extracting the digital biomarker feature data includes: determining, for each of the plurality of points, a ratio of the total magnitude of the acceleration and the magnitude of the z-component of the acceleration at the respective time; and deriving a statistical parameter from the plurality of determined ratios, the statistical parameter including a mean, a standard deviation, a percentile, a median, and a kurtosis; and determining the status or progression of the disease based on the determined statistical parameter.
A further aspect of the present invention provides a system for determining a status or progression of a disease, the system including: a mobile device having a an accelerometer, and a first processing unit; and a second processing unit; wherein: the accelerometer is configured to measure acceleration, and either the accelerometer, the first processing unit or the second processing unit is configured to generate acceleration data comprising a plurality of points, each point corresponding to the acceleration at a respective time; the first processing unit or the second processing unit is configured to extract digital biomarker feature data from the received input by: determining, for each of the plurality of points, a ratio of the total magnitude of the acceleration and the magnitude of the z-component of the acceleration at the respective time; and deriving a statistical parameter from the plurality of determined ratios, the statistical parameter including a mean, a standard deviation, a percentile, a median, and a kurtosis; and the first processing unit or the second processing unit configured to determine a status or progression of the disease based on the statistical parameter.
It should be explicitly appreciated that the features of the four aspects of the invention set out here may be combined with the features of any of the “embodiments” set out above, except where clearly incompatible, or where context dictates otherwise. The features of these two aspects of the invention may also be combined with any of the subsequent disclosure.
In a related aspect of the disclosure, a machine learning system for determining at least one analysis model for predicting at least one target variable indicative of a disease status is proposed.
The machine learning system comprises:
The term “machine learning” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a method of using artificial intelligence (AI) for automatically model building of analytical models. The term “machine learning system” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a system comprising at least one processing unit such as a processor, microprocessor, or computer system configured for machine learning, in particular for executing a logic in a given algorithm. The machine learning system may be configured for performing and/or executing at least one machine learning algorithm, wherein the machine learning algorithm is configured for building the at least one analysis model based on the training data.
The term “analysis model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a mathematical model configured for predicting at least one target variable for at least one state variable. The analysis model may be a regression model or a classification model. The term “regression model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an analysis model comprising at least one supervised learning algorithm having as output a numerical value within a range. The term “classification model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an analysis model comprising at least one supervised learning algorithm having as output a classifier such as “μl” or “healthy”.
The term “target variable” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a clinical value which is to be predicted. The target variable value which is to be predicted may dependent on the disease whose presence or status is to be predicted. The target variable may be either numerical or categorical. For example, the target variable may be categorical and may be “positive” in case of presence of disease or “negative” in case of absence of the disease.
The target variable may be numerical such as at least one value and/or scale value.
For example, the disease whose status is to be predicted is multiple sclerosis. The term “multiple sclerosis (MS)” as used herein relates to disease of the central nervous system (CNS) that typically causes prolonged and severe disability in a subject suffering therefrom. There are four standardized subtype definitions of MS which are also encompassed by the term as used in accordance with the present invention: relapsing-remitting, secondary progressive, primary progressive and progressive relapsing. The term relapsing forms of MS is also used and encompasses relapsing-remitting and secondary progressive MS with superimposed relapses. The relapsing-remitting subtype is characterized by unpredictable relapses followed by periods of months to years of remission with no new signs of clinical disease activity. Deficits suffered during attacks (active status) may either resolve or leave sequelae. This describes the initial course of 85 to 90% of subjects suffering from MS. Secondary progressive MS describes those with initial relapsing-remitting MS, who then begin to have progressive neurological decline between acute attacks without any definite periods of remission. Occasional relapses and minor remissions may appear. The median time between disease onset and conversion from relapsing remitting to secondary progressive MS is about 19 years. The primary progressive subtype describes about 10 to 15% of subjects who never have remission after their initial MS symptoms. It is characterized by progressive of disability from onset, with no, or only occasional and minor, remissions and improvements. The age of onset for the primary progressive subtype is later than other subtypes. Progressive relapsing MS describes those subjects who, from onset, have a steady neurological decline but also suffer clear superimposed attacks. It is now accepted that this latter progressive relapsing phenotype is a variant of primary progressive MS (PPMS) and diagnosis of PPMS according to McDonald 2010 criteria includes the progressive relapsing variant.
Symptoms associated with MS include changes in sensation (hypoesthesia and par-aesthesia), muscle weakness, muscle spasms, difficulty in moving, difficulties with co-ordination and balance (ataxia), problems in speech (dysarthria) or swallowing (dysphagia), visual problems (nystagmus, optic neuritis and reduced visual acuity, or diplopia), fatigue, acute or chronic pain, bladder, sexual and bowel difficulties. Cognitive impairment of varying degrees as well as emotional symptoms of depression or unstable mood are also frequent symptoms. The main clinical measure of disability progression and symptom severity is the Expanded Disability Status Scale (EDSS). Further symptoms of MS are well known in the art and are described in the standard text books of medicine and neurology.
The term “progressing MS” as used herein refers to a condition, where the disease and/or one or more of its symptoms get worse over time. Typically, the progression is accompanied by the appearance of active statuses. The said progression may occur in all subtypes of the disease.
However, typically “progressing MS” shall be determined in accordance with the present invention in subjects suffering from relapsing-remitting MS.
Determining status of multiple sclerosis, generally comprises assessing at least one symptom associated with multiple sclerosis selected from a group consisting of: impaired fine motor abilities, pins an needs, numbness in the fingers, fatigue and changes to diurnal rhythms, gait problems and walking difficulty, cognitive impairment including problems with processing speed. Disability in multiple sclerosis may be quantified according to the expanded disability status scale (EDSS) as described in Kurtzke J F, “Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)”, November 1983, Neurology. 33 (11): 1444-52. doi:10.1212/WNL.33.11.1444. PMID 6685237. The target variable may be an EDSS value.
The term “expanded disability status scale (EDSS)” as used herein, thus, refers to a score based on quantitative assessment of the disabilities in subjects suffering from MS (Krutzke 1983). The EDSS is based on a neurological examination by a clinician. The EDSS quantifies disability in eight functional systems by assigning a Functional System Score (FSS) in each of these functional systems. The functional systems are the pyramidal system, the cerebellar system, the brainstem system, the sensory system, the bowel and bladder system, the visual system, the cerebral system and other (remaining) systems. EDSS steps 1.0 to 4.5 refer to subjects suffering from MS who are fully ambulatory, EDSS steps 5.0 to 9.5 characterize those with impairment to ambulation.
The clinical meaning of each possible result is the following:
For example, the disease whose status is to be predicted is spinal muscular atrophy.
The term “spinal muscular atrophy (SMA)” as used herein relates to a neuromuscular disease which is characterized by the loss of motor neuron function, typically, in the spinal chord. As a consequence of the loss of motor neuron function, typically, muscle atrophy occurs resulting in an early dead of the affected subjects. The disease is caused by an inherited genetic defect in the SMN1 gene. The SMN protein encoded by said gene is required for motor neuron survival. The disease is inherited in an autosomal recessive manner.
Symptoms associated with SMA include areflexia, in particular, of the extremities, muscle weakness and poor muscle tone, difficulties in completing developmental phases in childhood, as a consequence of weakness of respiratory muscles, breathing problems occurs as well as secretion accumulation in the lung, as well as difficulties in sucking, swallowing and feeding/eating. Four different types of SMA are known.
The infantile SMA or SMA1 (Werdnig-Hoffmann disease) is a severe form that manifests in the first months of life, usually with a quick and unexpected onset (“floppy baby syndrome”). A rapid motor neuron death causes inefficiency of the major body organs, in particular, of the respiratory system, and pneumonia-induced respiratory failure is the most frequent cause of death. Unless placed on mechanical ventilation, babies diagnosed with SMA1 do not generally live past two years of age, with death occurring as early as within weeks in the most severe cases, sometimes termed SMA0. With proper respiratory support, those with milder SMA1 phenotypes accounting for around 10% of SMA1 cases are known to live into adolescence and adulthood.
The intermediate SMA or SMA2 (Dubowitz disease) affects children who are never able to stand and walk but who are able to maintain a sitting position at least some time in their life. The onset of weakness is usually noticed some time between 6 and 18 months. The progress is known to vary.
Some people gradually grow weaker over time while others through careful maintenance avoid any progression. Scoliosis may be present in these children, and correction with a brace may help improve respiration. Muscles are weakened, and the respiratory system is a major concern. Life expectancy is somewhat reduced but most people with SMA2 live well into adulthood.
The juvenile SMA or SMA3 (Kugelberg-Welander disease) manifests, typically, after 12 months of age and describes people with SMA3 who are able to walk without support at some time, although many later lose this ability. Respiratory involvement is less noticeable, and life expectancy is normal or near normal.
The adult SMA or SMA4 manifests, usually, after the third decade of life with gradual weakening of muscles that affects proximal muscles of the extremities frequently requiring the person to use a wheelchair for mobility. Other complications are rare, and life expectancy is unaffected.
Typically, SMA in accordance with the present invention is SMA1 (Werdnig-Hoffmann disease), SMA2 (Dubowitz disease), SMA3 (Kugelberg-Welander diseases) or SMA4 SMA is typically diagnosed by the presence of the hypotonia and the absence of reflexes. Both can be measured by standard techniques by the clinician in a hospital including electromyography.
Sometimes, serum creatine kinase may be increased as a biochemical parameter. Moreover, genetic testing is also possible, in particular, as prenatal diagnostics or carrier screening. Moreover, a critical parameter in SMA management is the function of the respiratory system. The function of the respiratory system can be, typically, determined by measuring the forced vital capacity of the subject which will be indicative for the degree of impairment of the respiratory system as a consequence of SMA.
The term “forced vital capacity (FVC)” as used herein refers to is the volume in liters of air that can forcibly be blown out after full inspiration by a subject. It is, typically, determined by spirometry in a hospital or at a doctor's residency using spirometric devices.
Determining status of spinal muscular atrophy, generally comprises assessing at least one symptom associated with spinal muscular atrophy selected from a group consisting of: hypotonia and muscle weakness, fatigue and changes to diurnal rhythms. A measure for status of spinal muscular atrophy may be the Forced vital capacity (FVC). The FVC may be a quantitative measure for volume of air that can forcibly be blown out after full inspiration, measured in liters, see https://en.wikipedia.org/wiki/Spirometry. The target variable may be a FVC value.
For example, the disease whose status is to be predicted is Huntington's disease.
The term “Huntington's Disease (HD)” as used herein relates to an inherited neurological disorder accompanied by neuronal cell death in the central nervous system. Most prominently, the basal ganglia are affected by cell death. There are also further areas of the brain involved such as substantia nigra, cerebral cortex, hippocampus and the purkinje cells. All regions, typically, play a role in movement and behavioral control. The disease is caused by genetic mutations in the gene encoding Huntingtin. Huntingtin is a protein involved in various cellular functions and interacts with over 100 other proteins. The mutated Huntingtin appears to be cytotoxic for certain neuronal cell types. Mutated Huntingtin is characterized by a poly glutamine region caused by a trinucleotide repeat in the Huntingtin gene. A repeat of more than 36 glutamine residues in the poly glutamine region of the protein results in the disease causing Huntingtin protein.
The symptoms of the disease most commonly become noticeable in the mid-age, but can begin at any age from infancy to the elderly. In early stages, symptoms involve subtle changes in personality, cognition, and physical skills. The physical symptoms are usually the first to be noticed, as cognitive and behavioral symptoms are generally not severe enough to be recognized on their own at said early stages. Almost everyone with HD eventually exhibits similar physical symptoms, but the onset, progression and extent of cognitive and behavioral symptoms vary significantly between individuals.
The most characteristic initial physical symptoms are jerky, random, and uncontrollable movements called chorea. Chorea may be initially exhibited as general restlessness, small unintentionally initiated or uncompleted motions, lack of coordination, or slowed saccadic eye movements. These minor motor abnormalities usually precede more obvious signs of motor dysfunction by at least three years. The clear appearance of symptoms such as rigidity, writhing motions or abnormal posturing appear as the disorder progresses. These are signs that the system in the brain that is responsible for movement has been affected. Psychomotor functions become increasingly impaired, such that any action that requires muscle control is affected. Common consequences are physical instability, abnormal facial expression, and difficulties chewing, swallowing, and speaking. Consequently, eating difficulties and sleep disturbances are also accompanying the disease. Cognitive abilities are also impaired in a progressive manner. Impaired are executive functions, cognitive flexibility, abstract thinking, rule acquisition, and proper action/reaction capabilities. In more pronounced stages, memory deficits tend to appear including short-term memory deficits to long-term memory difficulties. Cognitive problems worsen over time and will ultimately turn into dementia. Psychiatric complications accompanying HD are anxiety, depression, a reduced display of emotions (blunted affect), egocentrism, aggression, and compulsive behavior, the latter of which can cause or worsen addictions, including alcoholism, gambling, and hypersexuality.
There is no cure for HD. There are supportive measurements in disease management depending on the symptoms to be addressed. Moreover, a number of drugs are used to ameliorate the disease, its progression or the symptoms accompanying it. Tetrabenazine is approved for treatment of HD, include neuroleptics and benzodiazepines are used as drugs that help to reduce chorea, amantadine or remacemide are still under investigation but have shown preliminary positive results. Hypokinesia and rigidity, especially in juvenile cases, can be treated with antiparkinsonian drugs, and myoclonic hyperkinesia can be treated with valproic acid. Ethyl-eicosapentoic acid was found to enhance the motor symptoms of patients, however, its long-term effects need to be revealed.
The disease can be diagnosed by genetic testing. Moreover, the severity of the disease can be staged according to Unified Huntington's Disease Rating Scale (UHDRS). This scale system addresses four components, i.e. the motor function, the cognition, behavior and functional abilities.
The motor function assessment includes assessment of ocular pursuit, saccade initiation, saccade velocity, dysarthria, tongue protrusion, maximal dystonia, maximal chorea, retropulsion pull test, finger taps, pronate/supinate hands, luria, rigidity arms, bradykinesia body, gait, and tandem walking and can be summarized as total motor score (TMS). The motoric functions must be investigated and judged by a medical practitioner.
Determining status of Huntington's disease generally comprises assessing at least one symptom associated with Huntington's disease selected from a group consisting of: Psychomotor slowing, chorea (jerking, writhing), progressive dysarthria, rigidity and dystonia, social withdrawal, progressive cognitive impairment of processing speed, attention, planning, visual-spatial processing, learning (though intact recall), fatigue and changes to diurnal rhythms. A measure for status of is a total motor score (TMS). The target variable may be a total motor score (TMS) value. The term “total motor score (TMS)” as used herein, thus, refers to a score based on assessment of ocular pursuit, saccade initiation, saccade velocity, dysarthria, tongue protrusion, maximal dystonia, maximal chorea, retropulsion pull test, finger taps, pronate/supinate hands, luria, rigidity arms, bradykinesia body, gait, and tandem walking.
The term “state variable” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an input variable which can be filled in the prediction model such as data derived by medical examination and/or self-examination by a subject. The state variable may be determined in at least one active test and/or in at least one passive monitoring. For example, the state variable may be determined in an active test such as at least one cognition test and/or at least one hand motor function test and/or or at least one mobility test.
The term “subject” as used herein, typically, relates to mammals. The subject in accordance with the present invention may, typically, suffer from or shall be suspected to suffer from a disease, i.e. it may already show some or all of the negative symptoms associated with the said disease. In an embodiment of the invention said subject is a human.
The state variable may be determined by using at least one mobile device of the subject. The term “mobile device” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term may specifically refer, without limitation, to a mobile electronics device, more specifically to a mobile communication device comprising at least one processor. The mobile device may specifically be a cell phone or smartphone. The mobile device may also refer to a tablet computer or any other type of portable computer. The mobile device may comprise a data acquisition unit which may be configured for data acquisition. The mobile device may be configured for detecting and/or measuring either quantitatively or qualitatively physical parameters and transform them into electronic signals such as for further processing and/or analysis. For this purpose, the mobile device may comprise at least one sensor. It will be understood that more than one sensor can be used in the mobile device, i.e. at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine or at least ten or even more different sensors. The sensor may be at least one sensor selected from the group consisting of: at least one gyroscope, at least one magnetometer, at least one accelerometer, at least one proximity sensor, at least one thermometer, at least one pedometer, at least one fingerprint detector, at least one touch sensor, at least one voice recorder, at least one light sensor, at least one pressure sensor, at least one location data detector, at least one camera, at least one GPS, and the like. The mobile device may comprise the processor and at least one database as well as software which is tangibly embedded to said device and, when running on said device, carries out a method for data acquisition. The mobile device may comprise a user interface, such as a display and/or at least one key, e.g. for performing at least one task requested in the method for data acquisition.
The term “predicting” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to determining at least one numerical or categorical value indicative of the disease status for the at least one state variable. In particular, the state variable may be filled in the analysis as input and the analysis model may be configured for performing at least one analysis on the state variable for determining the at least one numerical or categorical value indicative of the disease status. The analysis may comprise using the at least one trained algorithm.
The term “determining at least one analysis model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to building and/or creating the analysis model.
The term “disease status” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to health condition and/or medical condition and/or disease stage. For example, the disease status may be healthy or ill and/or presence or absence of disease. For example, the disease status may be a value relating to a scale indicative of disease stage. The term “indicative of a disease status” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to information directly relating to the disease status and/or to information indirectly relating to the disease status, e.g. information which need further analysis and/or processing for deriving the disease status. For example, the target variable may be a value which need to be compared to a table and/or lookup table for determine the disease status.
The term “communication interface” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an item or element forming a boundary configured for transferring information. In particular, the communication interface may be configured for transferring information from a computational device, e.g. a computer, such as to send or output information, e.g. onto another device. Additionally or alternatively, the communication interface may be configured for transferring information onto a computational device, e.g. onto a computer, such as to receive information. The communication interface may specifically provide means for transferring or exchanging information. In particular, the communication interface may provide a data transfer connection, e.g. Bluetooth, NFC, inductive coupling or the like. As an example, the communication interface may be or may comprise at least one port comprising one or more of a network or internet port, a USB-port and a disk drive. The communication interface may be at least one web interface.
The term “input data” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to experimental data used for model building. The input data comprises the set of historical digital biomarker feature data. The term “biomarker” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a measurable characteristic of a biological state and/or biological condition. The term “feature” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a measurable property and/or characteristic of a symptom of the disease on which the prediction is based. In particular, all features from all tests may be considered and the optimal set of features for each prediction is determined. Thus, all features may be considered for each disease. The term “digital biomarker feature data” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to experimental data determined by at least one digital device such as by a mobile device which comprises a plurality of different measurement values per subject relating to symptoms of the disease. The digital biomarker feature data may be determined by using at least one mobile device. With respect to the mobile device and determining of digital biomarker feature data with the mobile device reference is made to the description of the determination of the state variable with the mobile device above. The set of historical digital biomarker feature data comprises a plurality of measured values per subject indicative of the disease status to be predicted. The term “historical” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to the fact that the digital biomarker feature data was determined and/or collected before model building such as during at least one test study. For example, for model building for predicting at least one target indicative of multiple sclerosis the digital biomarker feature data may be data from Floodlight POC study. For example, for model building for predicting at least one target indicative of spinal muscular atrophy the digital biomarker feature data may be data from OLEOS study. For example, for model building for predicting at least one target indicative of Huntington's disease the digital biomarker feature data may be data from HD OLE study, ISIS 44319-CS2. The input data may be determined in at least one active test and/or in at least one passive monitoring.
For example, the input data may be determined in an active test using at least one mobile device such as at least one cognition test and/or at least one hand motor function test and/or or at least one mobility test.
The input data further may comprise target data. The term “target data” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to data comprising clinical values to predict, in particular one clinical value per subject.
The target data may be either numerical or categorical. The clinical value may directly or indirectly refer to the status of the disease.
The processing unit may be configured for extracting features from the input data. The term “extracting features” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to at least one process of determining and/or deriving features from the input data. Specifically, the features may be pre-defined, and a subset of features may be selected from an entire set of possible features. The extracting of features may comprise one or more of data aggregation, data reduction, data transformation and the like. The processing unit may be configured for ranking the features. The term “ranking features” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to assigning a rank, in particular a weight, to each of the features depending on predefined criteria. For example, the features may be ranked with respect to their relevance, i.e. with respect to correlation with the target variable, and/or the features may be ranked with respect to redundancy, i.e. with respect to correlation between features. The processing unit may be configured for ranking the features by using a maximum-relevance-minimum-redundancy technique. This method ranks all features using a trade-off between relevance and redundancy. Specifically, the feature selection and ranking may be performed as described in Ding C., Peng H. “Minimum redundancy feature selection from microarray gene expression data”, J Bioinform Comput Biol. 2005 April; 3 (2):185-205, PubMed PMID:15852500. The feature selection and ranking may be performed by using a modified method compared to the method described in Ding et al. The maximum correlation coefficient may be used rather than the mean correlation coefficient and an addition transformation may be applied to it. In case of a regression model as analysis model the transformation the value of the mean correlation coefficient may be raised to the 5th power. In case of a classification model as analysis model the value of the mean correlation coefficient may be multiplied by 10.
The term “model unit” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to at least one data storage and/or storage unit configured for storing at least one machine learning model. The term “machine learning model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to at least one trainable algorithm. The model unit may comprise a plurality of machine learning models, e.g. different machine learning models for building the regression model and machine learning models for building the classification model. For example, the analysis model may be a regression model and the algorithm of the machine learning model may be at least one algorithm selected from the group consisting of: k nearest neighbors (kNN); linear regression; partial last-squares (PLS); random forest (RF); and extremely randomized Trees (XT). For example, the analysis model may be a classification model and the algorithm of the machine learning model may be at least one algorithm selected from the group consisting of: k nearest neighbors (kNN); support vector machines (SVM); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA); naïve Bayes (NB); random forest (RF); and extremely randomized Trees (XT).
The term “processing unit” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary logic circuitry configured for performing operations of a computer or system, and/or, generally, to a device which is configured for performing calculations or logic operations. The processing unit may comprise at least one processor. In particular, the processing unit may be configured for processing basic instructions that drive the computer or system. As an example, the processing unit may comprise at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or a numeric coprocessor, a plurality of registers and a memory, such as a cache memory. In particular, the processing unit may be a multi-core processor. The processing unit may be configured for machine learning. The processing unit may comprise a Central Processing Unit (CPU) and/or one or more Graphics Processing Units (GPUs) and/or one or more Application Specific Integrated Circuits (ASICs) and/or one or more Tensor Processing Units (TPUs) and/or one or more field-programmable gate arrays (FPGAs) or the like.
The processing unit may be configured for pre-processing the input data. The pre-processing may comprise at least one filtering process for input data fulfilling at least one quality criterion. For example, the input data may be filtered to remove missing variables. For example, the pre-processing may comprise excluding data from subjects with less than a pre-defined minimum number of observations.
The term “training data set” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a subset of the input data used for training the machine learning model. The term “test data set” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to another subset of the input data used for testing the trained machine learning model. The training data set may comprise a plurality of training data sets. In particular, the training data set comprises a training data set per subject of the input data. The test data set may comprise a plurality of test data sets. In particular, the test data set comprises a test data set per subject of the input data. The processing unit may be configured for generating and/or creating per subject of the input data a training data set and a test data set, wherein the test data set per subject may comprise data only of that subject, whereas the training data set for that subject comprises all other input data.
The processing unit may be configured for performing at least one data aggregation and/or data transformation on both of the training data set and the test data set for each subject. The transformation and feature ranking steps may be performed without splitting into training data set and test data set. This may allow to enable interference of e.g. important feature from the data.
The processing unit may be configured for one or more of at least one stabilizing transformation; at least one aggregation; and at least one normalization for the training data set and for the test data set.
For example, the processing unit may be configured for subject-wise data aggregation of both of the training data set and the test data set, wherein a mean value of the features is determined for each subject.
For example, the processing unit may be configured for variance stabilization, wherein for each feature at least one variance stabilizing function is applied. The variance stabilizing function may be at least one function selected from the group consisting of: a logistic, which may be used if all values are greater 300 and no values are between 0 and 1; a logit, which may be used if all values are between 0 and 1, inclusive; a sigmoid; a log 10, which may be used if considered when all values>=0. The processing unit may be configured for transforming values of each feature using each of the variance transformation functions. The processing unit may be configured for evaluating each of the resulting distributions, including the original one, using a certain criterion. In case of a classification model as analysis model, i.e. when the target variable is discrete, said criterion may be to what extent the obtained values are able to separate the different classes. Specifically, the maximum of all class-wise mean silhouette values may be used for this end. In case of a regression model as analysis model, the criterion may be a mean absolute error obtained after regression of values, which were obtained by applying the variance stabilizing function, against the target variable. Using this selection criterion, processing unit may be configured for determining the best possible transformation, if any are better than the original values, on the training data set. The best possible transformation can be subsequently applied to the test data set.
For example, the processing unit may be configured for z-score transformation, wherein for each transformed feature the mean and standard deviations are determined on the training data set, wherein these values are used for z-score transformation on both the training data set and the test data set.
For example, the processing unit may be configured for performing three data transformation steps on both the training data set and the test data set, wherein the transformation steps comprise: 1. subject-wise data aggregation; 2. variance stabilization; 3. z-score transformation.
The processing unit may be configured for determining and/or providing at least one output of the ranking and transformation steps. For example, the output of the ranking and transformation steps may comprise at least one diagnostics plots. The diagnostics plot may comprise at least one principal component analysis (PCA) plot and/or at least one pair plot comparing key statistics related to the ranking procedure.
The processing unit is configured for determining the analysis model by training the machine learning model with the training data set. The term “training the machine learning model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of determining parameters of the algorithm of machine learning model on the training data set. The training may comprise at least one optimization or tuning process, wherein a best parameter combination is determined. The training may be performed iteratively on the training data sets of different subjects. The processing unit may be configured for considering different numbers of features for determining the analysis model by training the machine learning model with the training data set. The algorithm of the machine learning model may be applied to the training data set using a different number of features, e.g. depending on their ranking. The training may comprise n-fold cross validation to get a robust estimate of the model parameters. The training of the machine learning model may comprise at least one controlled learning process, wherein at least one hyper-parameter is chosen to control the training process. If necessary the training is step is repeated to test different combinations of hyper-parameters.
In particular subsequent to the training of the machine learning model, the processing unit is configured for predicting the target variable on the test data set using the determined analysis model. The term “determined analysis model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to the trained machine learning model. The processing unit may be configured for predicting the target variable for each subject based on the test data set of that subject using the determined analysis model. The processing unit may be configured for predicting the target variable for each subject on the respective training and test data sets using the analysis model. The processing unit may be configured for recording and/or storing both the predicted target variable per subject and the true value of the target variable per subject, for example, in at least one output file. The term “true value of the target variable” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to the real or actual value of the target variable of that subject, which may be determined from the target data of that subject.
The processing unit is configured for determining performance of the determined analysis model based on the predicted target variable and the true value of the target variable of the test data set.
The term “performance” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to suitability of the determined analysis model for predicting the target variable. The performance may be characterized by deviations between predicted target variable and true value of the target variable. The machine learning system may comprises at least one output interface. The output interface may be designed identical to the communication interface and/or may be formed integral with the communication interface. The output interface may be configured for providing at least one output. The output may comprise at least one information about the performance of the determined analysis model. The information about the performance of the determined analysis model may comprises one or more of at least one scoring chart, at least one predictions plot, at least one correlations plot, and at least one residuals plot.
The model unit may comprise a plurality of machine learning models, wherein the machine learning models are distinguished by their algorithm. For example, for building a regression model the model unit may comprise the following algorithms k nearest neighbors (kNN), linear regression, partial last-squares (PLS), random forest (RF), and extremely randomized Trees (XT). For example, for building a classification model the model unit may comprise the following algorithms k nearest neighbors (kNN), support vector machines (SVM), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), naïve Bayes (NB), random forest (RF), and extremely randomized Trees (XT). The processing unit may be configured for determining a analysis model for each of the machine learning models by training the respective machine learning model with the training data set and for predicting the target variables on the test data set using the determined analysis models.
The processing unit may be configured for determining performance of each of the determined analysis models based on the predicted target variables and the true value of the target variable of the test data set. In case of building a regression model, the output provided by the processing unit may comprise one or more of at least one scoring chart, at least one predictions plot, at least one correlations plot, and at least one residuals plot. The scoring chart may be a box plot depicting for each subject a mean absolute error from both the test and training data set and for each type of regressor, i.e. the algorithm which was used, and number of features selected. The predictions plot may show for each combination of regressor type and number of features, how well the predicted values of the target variable correlate with the true value, for both the test and the training data. The correlations plot may show the Spearman correlation coefficient between the predicted and true target variables, for each regressor type, as a function of the number of features included in the model. The residuals plot may show the correlation between the predicted target variable and the residual for each combination of regressor type and number of features, and for both the test and training data. The processing unit may be configured for determining the analysis model having the best performance, in particular based on the output.
In case of building a classification model, the output provided by the processing unit may comprise the scoring chart, showing in a box plot for each subject the mean F1 performance score, also denoted as F-score or F-measure, from both the test and training data and for each type of regressor and number of features selected. The processing unit may be configured for determining the analysis model having the best performance, in particular based on the output.
In a further related aspect of the disclosure, a computer implemented method for determining at least one analysis model for predicting at least one target variable indicative of a disease status is proposed. In the method a machine learning system according to the present invention is used.
Thus, with respect to embodiments and definitions of the method reference is made to the description of the machine learning system above or as described in further detail below.
The method comprises the following method steps which, specifically, may be performed in the given order. Still, a different order is also possible. It is further possible to perform two or more of the method steps fully or partially simultaneously. Further, one or more or even all of the method steps may be performed once or may be performed repeatedly, such as repeated once or several times.
Further, the method may comprise additional method steps which are not listed.
The method comprises the following steps:
In step c) a plurality of analysis models may be determined by training a plurality of machine learning models with the training data set. The machine learning models may be distinguished by their algorithm. In step d) a plurality of target variables may be predicted on the test data set using the determined analysis models. In step e) the performance of each of the determined analysis models may be determined based on the predicted target variables and the true value of the target variable of the test data set. The method further may comprise determining the analysis model having the best performance.
Further disclosed and proposed herein is a computer program for determining at least one analysis model for predicting at least one target variable indicative of a disease status including computer-executable instructions for performing the method according to the present invention in one or more of the embodiments enclosed herein when the program is executed on a computer or computer network. Specifically, the computer program may be stored on a computer-readable data carrier and/or on a computer-readable storage medium. The computer program is configured to perform at least steps b) to e) of the method according to the present invention in one or more of the embodiments enclosed herein.
As used herein, the terms “computer-readable data carrier” and “computer-readable storage medium” specifically may refer to non-transitory data storage means, such as a hardware storage medium having stored thereon computer-executable instructions. The computer-readable data carrier or storage medium specifically may be or may comprise a storage medium such as a random-access memory (RAM) and/or a read-only memory (ROM).
Thus, specifically, one, more than one or even all of method steps b) to e) as indicated above may be performed by using a computer or a computer network, preferably by using a computer program.
Further disclosed and proposed herein is a computer program product having program code means, in order to perform the method according to the present invention in one or more of the embodiments enclosed herein when the program is executed on a computer or computer network.
Specifically, the program code means may be stored on a computer-readable data carrier and/or on a computer-readable storage medium.
Further disclosed and proposed herein is a data carrier having a data structure stored thereon, which, after loading into a computer or computer network, such as into a working memory or main memory of the computer or computer network, may execute the method according to one or more of the embodiments disclosed herein.
Further disclosed and proposed herein is a computer program product with program code means stored on a machine-readable carrier, in order to perform the method according to one or more of the embodiments disclosed herein, when the program is executed on a computer or computer network. As used herein, a computer program product refers to the program as a tradable product.
The product may generally exist in an arbitrary format, such as in a paper format, or on a computer-readable data carrier and/or on a computer-readable storage medium. Specifically, the computer program product may be distributed over a data network.
Finally, disclosed and proposed herein is a modulated data signal which contains instructions readable by a computer system or computer network, for performing the method according to one or more of the embodiments disclosed herein.
Referring to the computer-implemented aspects of the invention, one or more of the method steps or even all of the method steps of the method according to one or more of the embodiments disclosed herein may be performed by using a computer or computer network. Thus, generally, any of the method steps including provision and/or manipulation of data may be performed by using a computer or computer network. Generally, these method steps may include any of the method steps, typically except for method steps requiring manual work, such as providing the samples and/or certain aspects of performing the actual measurements.
Specifically, further disclosed herein are:
In a further aspect of the present invention a use of a machine learning system according to according to one or more of the embodiments disclosed herein is proposed for predicting one or more of an expanded disability status scale (EDSS) value indicative of multiple sclerosis, a forced vital capacity (FVC) value indicative of spinal muscular atrophy, or a total motor score (TMS) value indicative of Huntington's disease.
The devices and methods according to the present invention have several advantages over known methods for predicting disease status. The use of a machine learning system may allow to analyze large amount of complex input data, such as data determined in several and large test studies, and allow to determine analysis models which allow delivering fast, reliable and accurate results.
Summarizing and without excluding further possible embodiments, the following additional embodiments may be envisaged, which may be combined with any of the previous embodiments:
Additional embodiment 1: A machine learning system for determining at least one analysis model for predicting at least one target variable indicative of a disease status comprising:
Additional embodiment 2: The machine learning system according to the preceding embodiment, wherein the analysis model is a regression model or a classification model.
Additional embodiment 3: The machine learning system according to the preceding embodiment, wherein the analysis model is a regression model, wherein the algorithm of the machine learning model is at least one algorithm selected from the group consisting of: k nearest neighbors (kNN); linear regression; partial last-squares (PLS); random forest (RF); and extremely randomized Trees (XT), or wherein the analysis model is a classification model, wherein the algorithm of the machine learning model is at least one algorithm selected from the group consisting of: k nearest neighbors (kNN); support vector machines (SVM); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA); naïve Bayes (NB); random forest (RF); and extremely randomized Trees (XT).
Additional embodiment 4: The machine learning system according to any one of the preceding embodiments, wherein the model unit comprises a plurality of machine learning models, wherein the machine learning models are distinguished by their algorithm.
Additional embodiment 5: The machine learning system according to the preceding embodiment, wherein the processing unit is configured for determining a analysis model for each of the machine learning models by training the respective machine learning model with the training data set and for predicting the target variables on the test data set using the determined analysis models, wherein the processing unit is configured for determining performance of each of the determined analysis models based on the predicted target variables and the true value of the target variable of the test data set, wherein the processing unit is configured for determining the analysis model having the best performance.
Additional embodiment 6: The machine learning system according to any one of the preceding embodiments, wherein the target variable is a clinical value to be predicted, wherein the target variable is either numerical or categorical.
Additional embodiment 7: The machine learning system according to any one of the preceding embodiments, wherein the disease whose status is to be predicted is multiple sclerosis and the target variable is an expanded disability status scale (EDSS) value, or wherein the disease whose status is to be predicted is spinal muscular atrophy and the target variable is a forced vital capacity (FVC) value, or wherein the disease whose status is to be predicted is Huntington's disease and the target variable is a total motor score (TMS) value.
Additional embodiment 8: The machine learning system according to any one of the preceding embodiments, wherein the processing unit is configured for generating and/or creating per subject of the input data a training data set and a test data set, wherein the test data set comprises data of one subject, wherein the training data set comprises the other input data.
Additional embodiment 9: The machine learning system according to any one of the preceding embodiments, wherein the processing unit is configured for extracting features from the input data, wherein the processing unit is configured for ranking the features by using a maximum-relevance-minimum-redundancy technique.
Additional embodiment 10: The machine learning system according to the preceding embodiment, wherein the processing unit is configured for considering different numbers of features for determining the analysis model by training the machine learning model with the training data set.
Additional embodiment 11: The machine learning system according to any one of the preceding embodiments, wherein the processing unit is configured for pre-processing the input data, wherein the pre-processing comprises at least one filtering process for input data fulfilling at least one quality criterion.
Additional embodiment 12: The machine learning system according to any one of the preceding embodiments, wherein the processing unit is configured for performing one or more of at least one stabilizing transformation; at least one aggregation; and at least one normalization for the training data set and for the test data set.
Additional embodiment 13: The machine learning system according to any one of the preceding embodiments, wherein the machine learning system comprises at least one output interface, wherein the output interface is configured for providing at least one output, wherein the output comprises at least one information about the performance of the determined analysis model.
Additional embodiment 14: The machine learning system according to the preceding embodiment, wherein the information about the performance of the determined analysis model comprises one or more of at least one scoring chart, at least one predictions plot, at least one correlations plot, and at least one residuals plot.
Additional embodiment 15: A computer-implemented method for determining at least one analysis model for predicting at least one target variable indicative of a disease status, wherein in the method a machine learning system according to any one of the preceding embodiments is used, wherein the method comprises the following steps:
Additional embodiment 16: The method according to the preceding embodiment, wherein in step c) a plurality of analysis models is determined by training a plurality of machine learning models with the training data set, wherein the machine learning models are distinguished by their algorithm, wherein in step d) a plurality of target variables is predicted on the test data set using the determined analysis models, wherein in step e) the performance of each of the determined analysis models is determined based on the predicted target variables and the true value of the target variable of the test data set, wherein the method further comprises determining the analysis model having the best performance.
Additional embodiment 17: Computer program for determining at least one analysis model for predicting at least one target variable indicative of a disease status, configured for causing a computer or computer network to fully or partially perform the method for determining at least one analysis model for predicting at least one target variable indicative of a disease status according to any one of the preceding embodiments referring to a method, when executed on the computer or computer network, wherein the computer program is configured to perform at least steps b) to e) of the method for determining at least one analysis model for predicting at least one target variable indicative of a disease status according to any one of the preceding embodiments referring to a method.
Additional embodiment 18: A computer-readable storage medium comprising instructions which, when executed by a computer or computer network cause to carry out at least steps b) to e) of the method according to any one of the preceding method embodiments.
Additional embodiment 19: Use of a machine learning system according to any one of the preceding embodiments referring to a machine learning system for determining an analysis model for predicting one or more of an expanded disability status scale (EDSS) value indicative of multiple sclerosis, a forced vital capacity (FVC) value indicative of spinal muscular atrophy, or a total motor score (TMS) value indicative of Huntington's disease.
Further optional features and embodiments will be disclosed in more detail in the subsequent description of embodiments, preferably in conjunction with the dependent claims. Therein, the respective optional features may be realized in an isolated fashion as well as in any arbitrary feasible combination, as the skilled person will realize. The scope of the invention is not restricted by the preferred embodiments. The embodiments are schematically depicted in the Figures. Therein, identical reference numbers in these Figures refer to identical or functionally comparable elements.
In the drawings:
ICC(2,1) indicate comparable test-retest reliability for the (A) dominant hand and (B) non-dominant hand.
The analysis model may be a mathematical model configured for predicting at least one target variable for at least one state variable. The analysis model may be a regression model or a classification model. The regression model may be an analysis model comprising at least one supervised learning algorithm having as output a numerical value within a range. The classification model may be an analysis model comprising at least one supervised learning algorithm having as output a classifier such as “μl” or “healthy”.
The target variable value which is to be predicted may dependent on the disease whose presence or status is to be predicted. The target variable may be either numerical or categorical. For example, the target variable may be categorical and may be “positive” in case of presence of disease or “negative” in case of absence of the disease. The disease status may be a health condition and/or a medical condition and/or a disease stage. For example, the disease status may be healthy or ill and/or presence or absence of disease. For example, the disease status may be a value relating to a scale indicative of disease stage. The target variable may be numerical such as at least one value and/or scale value. The target variable may directly relate to the disease status and/or may indirectly relate to the disease status. For example, the target variable may need further analysis and/or processing for deriving the disease status. For example, the target variable may be a value which need to be compared to a table and/or lookup table for determine the disease status.
The machine learning system 110 comprises at least one processing unit 112 such as a processor, microprocessor, or computer system configured for machine learning, in particular for executing a logic in a given algorithm. The machine learning system 110 may be configured for performing and/or executing at least one machine learning algorithm, wherein the machine learning algorithm is configured for building the at least one analysis model based on the training data. The processing unit 112 may comprise at least one processor. In particular, the processing unit 112 may be configured for processing basic instructions that drive the computer or system. As an example, the processing unit 112 may comprise at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or a numeric coprocessor, a plurality of registers and a memory, such as a cache memory. In particular, the processing unit 112 may be a multi-core processor. The processing unit 112 may be configured for machine learning. The processing unit 112 may comprise a Central Processing Unit (CPU) and/or one or more Graphics Processing Units (GPUs) and/or one or more Application Specific Integrated Circuits (ASICs) and/or one or more Tensor Processing Units (TPUs) and/or one or more field-programmable gate arrays (FPGAs) or the like.
The machine learning system comprises at least one communication interface 114 configured for receiving input data. The communication interface 114 may be configured for transferring information from a computational device, e.g. a computer, such as to send or output information, e.g. onto another device. Additionally or alternatively, the communication interface 114 may be configured for transferring information onto a computational device, e.g. onto a computer, such as to receive information. The communication interface 114 may specifically provide means for transferring or exchanging information. In particular, the communication interface 114 may provide a data transfer connection, e.g. Bluetooth, NFC, inductive coupling or the like. As an example, the communication interface 114 may be or may comprise at least one port comprising one or more of a network or internet port, a USB-port and a disk drive. The communication interface 114 may be at least one web interface.
The input data comprises a set of historical digital biomarker feature data, wherein the set of historical digital biomarker feature data comprises a plurality of measured values indicative of the disease status to be predicted. The set of historical digital biomarker feature data comprises a plurality of measured values per subject indicative of the disease status to be predicted. For example, for model building for predicting at least one target indicative of multiple sclerosis the digital biomarker feature data may be data from Floodlight POC study. For example, for model building for predicting at least one target indicative of spinal muscular atrophy the digital biomarker feature data may be data from OLEOS study. For example, for model building for predicting at least one target indicative of Huntington's disease the digital biomarker feature data may be data from HD OLE study, ISIS 44319-CS2. The input data may be determined in at least one active test and/or in at least one passive monitoring. For example, the input data may be determined in an active test using at least one mobile device such as at least one cognition test and/or at least one hand motor function test and/or or at least one mobility test.
The input data further may comprise target data. The target data comprises clinical values to predict, in particular one clinical value per subject. The target data may be either numerical or categorical. The clinical value may directly or indirectly refer to the status of the disease.
The processing unit 112 may be configured for extracting features from the input data. The extracting of features may comprise one or more of data aggregation, data reduction, data transformation and the like. The processing unit 112 may be configured for ranking the features. For example, the features may be ranked with respect to their relevance, i.e. with respect to correlation with the target variable, and/or the features may be ranked with respect to redundancy, i.e. with respect to correlation between features. The processing unit 110 may be configured for ranking the features by using a maximum-relevance-minimum-redundancy technique. This method ranks all features using a trade-off between relevance and redundancy. Specifically, the feature selection and ranking may be performed as described in Ding C., Peng H. “Minimum redundancy feature selection from microarray gene expression data”, J Bioinform Comput Biol. 2005 April; 3 (2):185-205, PubMed PMID:15852500. The feature selection and ranking may be performed by using a modified method compared to the method described in Ding et al. The maximum correlation coefficient may be used rather than the mean correlation coefficient and an addition transformation may be applied to it. In case of a regression model as analysis model the transformation the value of the mean correlation coefficient may be raised to the 5th power. In case of a classification model as analysis model the value of the mean correlation coefficient may be multiplied by 10.
The machine learning system 110 comprises at least one model unit 116 comprising at least one machine learning model comprising at least one algorithm. The model unit 116 may comprise a plurality of machine learning models, e.g. different machine learning models for building the regression model and machine learning models for building the classification model. For example, the analysis model may be a regression model and the algorithm of the machine learning model may be at least one algorithm selected from the group consisting of: k nearest neighbors (kNN); linear regression; partial last-squares (PLS); random forest (RF); and extremely randomized Trees (XT). For example, the analysis model may be a classification model and the algorithm of the machine learning model may be at least one algorithm selected from the group consisting of: k nearest neighbors (kNN); support vector machines (SVM); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA); naïve Bayes (NB); random forest (RF); and extremely randomized Trees (XT).
The processing unit 112 may be configured for pre-processing the input data. The pre-processing 112 may comprise at least one filtering process for input data fulfilling at least one quality criterion. For example, the input data may be filtered to remove missing variables. For example, the pre-processing may comprise excluding data from subjects with less than a pre-defined minimum number of observations.
The processing unit 112 is configured for determining at least one training data set and at least one test data set from the input data set. The training data set may comprise a plurality of training data sets. In particular, the training data set comprises a training data set per subject of the input data. The test data set may comprise a plurality of test data sets. In particular, the test data set comprises a test data set per subject of the input data. The processing unit 112 may be configured for generating and/or creating per subject of the input data a training data set and a test data set, wherein the test data set per subject may comprise data only of that subject, whereas the training data set for that subject comprises all other input data.
The processing unit 112 may be configured for performing at least one data aggregation and/or data transformation on both of the training data set and the test data set for each subject. The transformation and feature ranking steps may be performed without splitting into training data set and test data set. This may allow to enable interference of e.g. important feature from the data. The processing unit 112 may be configured for one or more of at least one stabilizing transformation; at least one aggregation; and at least one normalization for the training data set and for the test data set. For example, the processing unit 112 may be configured for subject-wise data aggregation of both of the training data set and the test data set, wherein a mean value of the features is determined for each subject. For example, the processing unit 112 may be configured for variance stabilization, wherein for each feature at least one variance stabilizing function is applied. The variance stabilizing function may be at least one function selected from the group consisting of: a logistic, which may be used if all values are greater 300 and no values are between 0 and 1; a logit, which may be used if all values are between 0 and 1, inclusive; a sigmoid; a log 10, which may be used if considered when all values >=0. The processing unit 112 may be configured for transforming values of each feature using each of the variance transformation functions. The processing unit 112 may be configured for evaluating each of the resulting distributions, including the original one, using a certain criterion. In case of a classification model as analysis model, i.e. when the target variable is discrete, said criterion may be to what extent the obtained values are able to separate the different classes. Specifically, the maximum of all class-wise mean silhouette values may be used for this end. In case of a regression model as analysis model, the criterion may be a mean absolute error obtained after regression of values, which were obtained by applying the variance stabilizing function, against the target variable. Using this selection criterion, processing unit 112 may be configured for determining the best possible transformation, if any are better than the original values, on the training data set. The best possible transformation can be subsequently applied to the test data set. For example, the processing unit 112 may be configured for z-score transformation, wherein for each transformed feature the mean and standard deviations are determined on the training data set, wherein these values are used for z-score transformation on both the training data set and the test data set. For example, the processing unit 112 may be configured for performing three data transformation steps on both the training data set and the test data set, wherein the transformation steps comprise: 1. subject-wise data aggregation; 2. variance stabilization; 3. z-score transformation. The processing unit 112 may be configured for determining and/or providing at least one output of the ranking and transformation steps. For example, the output of the ranking and transformation steps may comprise at least one diagnostics plots. The diagnostics plot may comprise at least one principal component analysis (PCA) plot and/or at least one pair plot comparing key statistics related to the ranking procedure.
The processing unit 112 is configured for determining the analysis model by training the machine learning model with the training data set. The training may comprise at least one optimization or tuning process, wherein a best parameter combination is determined. The training may be performed iteratively on the training data sets of different subjects. The processing unit 112 may be configured for considering different numbers of features for determining the analysis model by training the machine learning model with the training data set. The algorithm of the machine learning model may be applied to the training data set using a different number of features, e.g. depending on their ranking. The training may comprise n-fold cross validation to get a robust estimate of the model parameters. The training of the machine learning model may comprise at least one controlled learning process, wherein at least one hyper-parameter is chosen to control the training process. If necessary the training is step is repeated to test different combinations of hyper-parameters.
In particular subsequent to the training of the machine learning model, the processing unit 112 is configured for predicting the target variable on the test data set using the determined analysis model. The processing unit 112 may be configured for predicting the target variable for each subject based on the test data set of that subject using the determined analysis model. The processing unit 112 may be configured for predicting the target variable for each subject on the respective training and test data sets using the analysis model. The processing unit 112 may be configured for recording and/or storing both the predicted target variable per subject and the true value of the target variable per subject, for example, in at least one output file.
The processing unit 112 is configured for determining performance of the determined analysis model based on the predicted target variable and the true value of the target variable of the test data set. The performance may be characterized by deviations between predicted target variable and true value of the target variable. The machine learning system 110 may comprises at least one output interface 118. The output interface 118 may be designed identical to the communication interface 114 and/or may be formed integral with the communication interface 114. The output interface 118 may be configured for providing at least one output. The output may comprise at least one information about the performance of the determined analysis model. The information about the performance of the determined analysis model may comprises one or more of at least one scoring chart, at least one predictions plot, at least one correlations plot, and at least one residuals plot.
The model unit 116 may comprise a plurality of machine learning models, wherein the machine learning models are distinguished by their algorithm. For example, for building a regression model the model unit 116 may comprise the following algorithms k nearest neighbors (kNN), linear regression, partial last-squares (PLS), random forest (RF), and extremely randomized Trees (XT). For example, for building a classification model the model unit 116 may comprise the following algorithms k nearest neighbors (kNN), support vector machines (SVM), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), naïve Bayes (NB), random forest (RF), and extremely randomized Trees (XT). The processing unit 112 may be configured for determining a analysis model for each of the machine learning models by training the respective machine learning model with the training data set and for predicting the target variables on the test data set using the determined analysis models.
In the prospective pilot study (FLOODLIGHT) the feasibility of conducting remote patient monitoring with the use of digital technology in patients with multiple sclerosis was evaluated. A study population was selected by using the following inclusion and exclusion criteria:
It is a primary objective of this study to show adherence to smartphone and smartwatch-based assessments quantified as compliance level (%) and to obtain feedback from patients and healthy controls on the smartphone and smartwatch schedule of assessments and the impact on their daily activities using a satisfaction questionnaire. Furthermore, additional objectives are addressed, in particular, the association between assessments conducted using the Floodlight Test and conventional MS clinical outcomes was determined, it was established if Floodlight measures can be used as a marker for disease activity/progression and are associated with changes in MRI and clinical outcomes over time and it was determined if the Floodlight Test Battery can differentiate between patients with and without MS, and between phenotypes in patients with MS.
In addition to the active tests and passive monitoring, the following assessments were performed at each scheduled clinic visit:
While performing in-clinic tests, patients and healthy controls were asked to carry/wear smartphone and smartwatch to collect sensor data along with in-clinic measures. In summary, the results of the study showed that patients are highly engaged with the smartphone- and smartwatch-based assessments. Moreover, there is a correlation between tests and in-clinic clinical outcome measures recorded at baseline which suggests that the smartphone-based Floodlight Test Battery shall become a powerful tool to continuously monitor MS in a real-world scenario. Further, the smartphone-based measurement of turning speed while walking and performing U-turns appeared to correlate with EDSS.
For
The following gives more detailed description of the tests. The tests are typically computer-implemented on a data acquisition device such as a mobile device as specified elsewhere herein.
The mobile device is, typically, adapted for performing or acquiring data from passive monitoring of all or a subset of activities In particular, the passive monitoring shall encompass monitoring one or more activities performed during a predefined window, such as one or more days or one or more weeks, selected from the group consisting of: measurements of gait, the amount of movement in daily routines in general, the types of movement in daily routines, general mobility in daily living and changes in moving behavior.
Typical passive monitoring performance parameters of interest:
The mobile device is also, typically, adapted for performing or acquiring a data from an computer-implemented Symbol Digit Modalities Test (eSDMT). The conventional paper SDMT version of the test consists of a sequence of 120 symbols to be displayed in a maximum 90 seconds and a reference key legend (3 versions are available) with 9 symbols in a given order and their respective matching digits from 1 to 9. The smartphone-based eSDMT is meant to be self-administered by patients and will use a sequence of symbols, typically, the same sequence of 110 symbols, and a random alternation (form one test to the next) between reference key legends, typically, the 3 reference key legends, of the paper/oral version of SDMT. The eSDMT similarly to the paper/oral version measures the speed (number of correct paired responses) to pair abstract symbols with specific digits in a predetermined time window, such as 90 seconds time. The test is, typically, performed weekly but could alternatively be performed at higher (e.g. daily) or lower (e.g. bi-weekly) frequency. The test could also alternatively encompass more than 110 symbols and more and/or evolutionary versions of reference key legends. The symbol sequence could also be administered randomly or according to any other modified pre-specified sequence.
Typical eSDMT performance parameters of interest:
A sensor-based (e.g. accelerometer, gyroscope, magnetometer, global positioning system [GPS]) and computer implemented test for measures of ambulation performances and gait and stride dynamics, in particular, the 2-Minute Walking Test (2MWT) and the Five U-Turn Test (5UTT).
In one embodiment, the mobile device is adapted to perform or acquire data from the Two-Minute Walking Test (2MWT). The aim of this test is to assess difficulties, fatigability or unusual patterns in long-distance walking by capturing gait features in a two-minute walk test (2MWT). Data will be captured from the mobile device. A decrease of stride and step length, increase in stride duration, increase in step duration and asymmetry and less periodic strides and steps may be observed in case of disability progression or emerging relapse. Arm swing dynamic while walking will also be assessed via the mobile device. The subject will be instructed to “walk as fast and as long as you can for 2 minutes but walk safely”. The 2MWT is a simple test that is required to be performed indoor or outdoor, on an even ground in a place where patients have identified they could walk straight for as far as >200 meters without U-turns. Subjects are allowed to wear regular footwear and an assistive device and/or orthotic as needed. The test is typically performed daily.
Typical 2MWT performance parameters of particular interest:
In another embodiment, the mobile device is adapted to perform or acquire data from the Five U-Turn Test (5UTT). The aim of this test is to assess difficulties or unusual patterns in performing U-turns while walking on a short distance at comfortable pace. The 5UTT is required to be performed indoor or outdoor, on an even ground where patients are instructed to “walk safely and perform five successive U-turns going back and forward between two points a few meters apart”. Gait feature data (change in step counts, step duration and asymmetry during U-turns, U-turn duration, turning speed and change in arm swing during U-turns) during this task will be captured by the mobile device. Subjects are allowed to wear regular footwear and an assistive device and/or orthotic as needed. The test is typically performed daily.
Typical 5UTT performance parameters of interest:
The following gives more detailed description of the tests. The tests are typically computer-implemented on a data acquisition device such as a mobile device as specified elsewhere herein.
The mobile device may be further adapted for performing or acquiring a data from a further test for distal motor function (so-called “draw a shape test”) configured to measure dexterity and distal weakness of the fingers. The dataset acquired from such test allow identifying the precision of finger movements, pressure profile and speed profile.
The aim of the “Draw a Shape” test is to assess fine finger control and stroke sequencing. The test is considered to cover the following aspects of impaired hand motor function: tremor and spasticity and impaired hand-eye coordination. The patients are instructed to hold the mobile device in the untested hand and draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity (linear, rectangular, circular, sinusoidal, and spiral; vide infra) with the second finger of the tested hand “as fast and as accurately as possible” within a maximum time of for instance 30 seconds. To draw a shape successfully the patient's finger has to slide continuously on the touchscreen and connect indicated start and end points passing through all indicated check points and keeping within the boundaries of the writing path as much as possible. The patient has maximum two attempts to successfully complete each of the 6 shapes. Test will be alternatingly performed with right and left hand. User will be instructed on daily alternation. The two linear shapes have each a specific number “a” of checkpoints to connect, i.e “a-1” segments. The square shape has a specific number “b” of checkpoints to connect, i.e. “b-1” segments. The circular shape has a specific number “c” of checkpoints to connect, i.e. “c-1” segments. The eight-shape has a specific number “d” of checkpoints to connect, i.e “d-1” segments. The spiral shape has a specific number “e” of checkpoints to connect, “e-1” segments. Completing the 6 shapes then implies to draw successfully a total of “(2a+b+c+d+e-6)” segments.
Based on shape complexity, the linear and square shapes can be associated with a weighting factor (Wf) of 1, circular and sinusoidal shapes a weighting factor of 2, and the spiral shape a weighting factor of 3. A shape which is successfully completed on the second attempt can be associated with a weighting factor of 0.5. These weighting factors are numerical examples which can be changed in the context of the present invention.
The distal motor function (so-called “squeeze a shape test”) may measure dexterity and distal weakness of the fingers. The dataset acquired from such test allow identifying the precision and speed of finger movements and related pressure profiles. The test may require calibration with respect to the movement precision ability of the subject first.
The aim of the Squeeze a Shape test is to assess fine distal motor manipulation (gripping & grasping) & control by evaluating accuracy of pinch closed finger movement. The test is considered to cover the following aspects of impaired hand motor function: impaired gripping/grasping function, muscle weakness, and impaired hand-eye coordination. The patients are instructed to hold the mobile device in the untested hand and by touching the screen with two fingers from the same hand (thumb+second or thumb+third finger preferred) to squeeze/pinch as many round shapes (i.e. tomatoes) as they can during 30 seconds. Impaired fine motor manipulation will affect the performance. Test will be alternatingly performed with right and left hand. User will be instructed on daily alternation.
Typical Squeeze a Shape test performance parameters of interest:
More typically, the Squeeze a Shape test and the Draw a Shape test are performed in accordance with the method of the present invention. Even more specifically, the performance parameters listed in the Table 1 below are determined.
In addition to the features outlined above, various other features may also be evaluated when performing a “squeeze a shape” or “pinching” test. These are described below. The following terms are used in the description of the additional features:
The features are as follows:
In all of the above cases, the test may be performed several times, and a statistical parameter such as the mean, standard deviation, kurtosis, median, and a percentile may be derived. Where a plurality of measurements are taken in this manner, a generic fatigue factor may be determined.
In some cases, the data acquisition device such as a mobile device may include an accelerometer, which may be configured to measure acceleration data during the period while the test is being performed. There are various useful features which can be extracted from the acceleration data too, as described below:
It should be stressed that, where possible, these acceleration-based features need not only be taken during a pinching or squeeze-a-shape, as they are able to yield clinically meaningful outputs independent of the kind of test during which they are extracted. This is especially true of the horizontalness and orientation stability parameters.
The data acquisition device may be further adapted for performing or acquiring a data from a further test for central motor function (so-called “voice test”) configured to measure proximal central motoric functions by measuring voicing capabilities.
The term “Cheer-the-Monster test”, as used herein, relates to a test for sustained phonation, which is, in an embodiment, a surrogate test for respiratory function assessments to address abdominal and thoracic impairments, in an embodiment including voice pitch variation as an indicator of muscular fatigue, central hypotonia and/or ventilation problems. In an embodiment, Cheer-the-Monster measures the participant's ability to sustain a controlled vocalization of an “aaah” sound. The test uses an appropriate sensor to capture the participant's phonation, in an embodiment a voice recorder, such as a microphone.
In an embodiment, the task to be performed by the subject is as follows: Cheer the Monster requires the participant to control the speed at which the monster runs towards his goal. The monster is trying to run as far as possible in 30 seconds. Subjects are asked to make as loud an “aaah” sound as they can, for as long as possible. The volume of the sound is determined and used to modulate the character's running speed. The game duration is 30 seconds so multiple “aaah” sounds may be used to complete the game if necessary.
The term “Tap the Monster test”, as used herein, relates to a test designed for the assessment of distal motor function in accordance with MFM D3 (Berard C et al. (2005), Neuromuscular Disorders 15:463). In an embodiment, the tests are specifically anchored to MFM tests 17 (pick up ten coins), 18 (go around the edge of a CD with a finger), 19 (pick up a pencil and draw loops) and 22 (place finger on the drawings), which evaluate dexterity, distal weakness/strength, and power. The game measures the participant's dexterity and movement speed. In an embodiment, the task to be performed by the subject is as follows: Subject taps on monsters appearing randomly at 7 different screen positions.
The system 100 may be used to implement at least one of a pinching test, and/or a draw-a-shape test, as have been described previously in this application. The aim of a pinching test is to assess fine distal motor manipulation (gripping and grasping), and control by evaluating accuracy of pinch closed finger movement. The test may cover the following aspects of impaired hand motor function: impaired gripping/grasping function, muscle weakness, and impaired hand-eye coordination. In order to perform the test, a patient is instructed to hold a mobile device in the untested hand (or by placing it on a table or other surface) and by touching the screen with two finger from the same hand (preferably the thumb+index finger/middle finger) to squeeze/pinch as many round shapes as they can during fixed time, e.g. 30 seconds. Round shapes are displayed at a random location within the game area. Impaired fine motor performance will affect the performance. The test may be performed alternatingly with the left hand and the right hand. The following terminology will be used when describing the pinching test:
Any or all of the following parameters may be defined:
It should be stressed that all of the features discussed earlier in the application may be used in conjunction with the system 100 shown in
We now discuss various features which can be used to determine whether a test is “complete”. For example, a test may be considered complete when the distance between the fingers is decreasing, the distance between the fingers becomes less than the pinch gap, and the distance between the fingers has decreased by at least the minimum change in separation between the fingers. In addition to determining whether the test is “complete”, the application may be configured to determine when the test is “successful”. For example, an attempt may be considered successful when the centre point between the two fingers is closer than a predetermined threshold, to the centre of the shape, or the centre of the bounding box. This predetermined threshold may be half of the pinch gap.
As has been discussed earlier in this application, three useful features may be extracted from draw-a-shape tests. These are illustrated in
Multiple sclerosis (MS) is a chronic inflammatory, autoimmune, demyelinating disease of the central nervous system.1 It can result in impairment of several functional domains, including upper extremity function. Studies suggest that up to 60-76% of people with MS (PwMS) experience or show signs of impaired upper extremity function during their disease.2-4 Such impairment can limit the ability to perform activities of daily living and thereby reduce quality of life.5 Thus, assessing upper extremity function plays an important role in monitoring the disease.6 Unfortunately, although upper extremity function strongly impacts the quality of life and is a critical measurement for patients with more pronounced disability, it is rarely measured in most therapeutic trials. Several different assessments are available to measure upper extremity function, or manual dexterity. These, among others, include the Strength-Dexterity test,7 the Grooved Pegboard,8 the Minnesota Dexterity Test (and its turning subtest),9 the Functional Dexterity Test10, 11 and the Nine-Hole Peg Test (9HPT).6 Due to its ease of use and favorable psychometric properties, the 9HPT has become a commonly used assessment of upper extremity function and it is included in the Multiple Sclerosis Functional Composite (MSFC).6, 12, 13 However, functional assessments such as the 9HPT are infrequently administered during clinic visits, thereby limiting their utility.14 Thus, there is a need for new assessments that can be taken remotely with minimal patient burden and that, therefore, can be administered more frequently. The Pinching Test was designed as an objective, ecologically valid, sensor-based assessment of upper extremity function that can be performed on a smartphone remotely at home without supervision.15 By taking advantage of the sensors embedded in smartphone devices, it enables the measurement of multiple characteristics of the pinching movement as opposed to providing a single summary score as with the 9HPT. The Pinching Test was first deployed in the clinical trial ‘Monitoring of Multiple Sclerosis Participants with the Use of Digital Technology (Smartphones and Smartwatches)—A Feasibility Study’ (NCT02952911).16 In this study, it was previously shown that two features—number of successful pinches and double touch asynchrony—correlated with the 9HPT, Expanded Disability Status Scale (EDSS),17 arm-related items of the 29-item Multiple Sclerosis Impact Scale (MSIS-29)18 and whole brain volume, and showed moderate-to-good test-retest reliability.15 By analyzing the richer data on upper extremity function presented here, this prior work is expanded upon by exploring an expanded range of features designed to probe different aspects of upper extremity functional impairment and to further characterize the use of the Pinching Test as an objective and remote assessment of upper extremity function. The expanded feature space's test-retest reliability is examined, along with its agreement with the standard clinical measures of MS disease state, and its ability to differentiate PwMS from healthy controls (HC). Furthermore, the shared and complementary information captured between the Pinching Test features is also evaluated.
The 24-week, prospective study assessed the feasibility of remotely monitoring MS with the Floodlight PoC app on a provisioned smartphone. The full study design, and inclusion and exclusion criteria have been previously reported16. Seventy-six PwMS and twenty-five HC aged 18-55 years were enrolled across two sites. PwMS were diagnosed with the 2010 revised McDonald criteria19 and had a baseline EDSS score between 0.0 and 5.5. The study included three clinic visits (baseline, Week 12, and Week 24 [end of study]) during which all study participants underwent standard clinical measures. PwMS were evaluated with the 9HPT, EDSS, MSIS-29, oral Symbol Digit Modalities Test (SDMT) and Fatigue Scale for Motor and Cognitive Functions (FSMC), while HC were evaluated with the 9HPT, oral SDMT and FSMC only. Additionally, during the baseline visit all study participants received a provisioned Samsung Galaxy S7 smartphone with the Floodlight PoC app preinstalled, and were instructed to perform daily smartphone-based tests, including the Pinching Test.
The Pinching Test, which forms the subject of this patent application, is designed to test motor, visual and cognitive aspects of upper extremity function.15 By evaluating the coordination of two fingers (the thumb and either the second or third finger), it assesses the ability which is required to grasp small objects such as keys, pens or door handles. To perform the test, the participants held their smartphones in one hand and used the other hand to pinch, or squeeze, as many tomato shapes as possible in 30 seconds, see
In total, 13 base features, 11 inertial measurement unit (IMU)-based features, and 13 fatigue features that are illustrative of the test were extracted from the raw touchscreen and accelerometer signals, see
In this patent application, we consider the results from the following features:
Definitions of these features, and other comparative features, are provided in the table which is distributed throughout
Base features capture overall impairment of upper extremity (number of performed pinches, number of successful pinches, fraction of successful attempts and fraction of two-finger attempts); finger coordination (double touch asynchrony and double lift asynchrony), pinching responsiveness (gap time); range of motion or pinching precision (finger path length); as well as muscle weakness, spasticity or tremor (finger path ratio, finger path velocity, distance between first or last points, pinch time). IMU-based features are based on either the mean, standard deviation, and kurtosis of the accelerometer magnitude of the untested hand holding the smartphone device or the smartphone's orientation, and were developed to capture signals arising from the coordination between the two hands, muscle weakness, or tremor. Fatigue features were computed for each base feature by measuring a difference in performance between the first and second half of the test.
Data processing
As the Pinching Test is unsupervised, it is important to identify individual test runs that were not performed in accordance with the test's instructions.15 Study participants were instructed to hold the phone in the untested hand while taking the Pinching Test; any test runs characterized by the phone lying on a hard surface, such as a table, were disregarded and considered invalid. Furthermore, to enable a meaningful assessment of upper extremity function, only study participants who contributed at least 20 valid test runs were retained for the analyses. Finally, the data were aggregated as follows:
Four separate analyses were conducted. While the data from the dominant and non-dominant hand were highly comparable (see
Test-retest reliability was assessed in PwMS by computing intraclass correlation coefficients (2,1) (ICC[2,1]), which considered all consecutive two-week windows.15 At least three valid test runs were required for each two-week window. Test-retest reliability was considered as poor (ICC<0.05), moderate (ICC=0.5 to 0.74), good (ICC=0.75 to 0.9) or excellent (ICC>0.9).21
The age- and sex-adjusted Spearman rank correlation analysis evaluated the agreement with standard clinical measures. The Pinching Test features were correlated against the 9HPT, EDSS, MSIS-29 arm items, oral SDMT, and FSMC. This analysis was limited to PwMS only as both EDDS and MSIS-29 were not collected in HC. The strength of the correlation was considered as not correlated (|r|<0.25), fair (|r|=0.25 to 0.49), moderate-to-good (|r|=0.5 to 0.75) or good-to excellent (|r|>0.75).22
In addition, two separate age- and sex-adjusted partial Spearman rank correlation analyses were conducted on 1) the base features, 9HPT and oral SDMT to assess whether the base features are primarily driven by a motor or cognitive component (or both) and 2) the fatigue features, 9HPT and FSMC to study whether the fatigue features are primarily measure upper extremity function or fatigue.
The age- and sex-adjusted known-groups validity analysis assessed the ability to differentiate between HC and PwMS subgroups and was evaluated using the Mann-Whitney U test, Cohen's d effect size, and the area under the receiver operating curve (AUC). Two PwMS subgroups were included: PwMS with normal 9HPT time at baseline (PwMS-Normal) and PwMS with abnormal 9HPT time at baseline (PwMS-Abnormal). The threshold for abnormal 9HPT was defined as the mean plus two standard deviations of the dominant-hand normative data of HC derived from Erasmus et al.23 Thus, all PwMS with a baseline 9HPT time below 22.15 seconds for the dominant hand were considered as PwMS-Normal, while all remaining PwMS were considered as PwMS-Abnormal. In a separate analysis, the ability of the fatigue features to differentiate between PwMS without and with physical fatigue, defined as at least mild levels of fatigue (≥22 points on the FSMC physical subscale),24 was studied. To evaluate the shared and complementary information between the Pinching Test features, a cross-sectional pairwise Spearman's rank correlation and a repeated-measures correlation were performed. In comparison to the pairwise correlation, the repeated-measures correlation analysis estimates an independent intercept for each subject, thereby minimizing a possible bias introduced by differences in disease severity between subjects. This was complemented by performing a principal component analysis and a factor analysis.
Statistical significance was set at p<0.05 after false discover rate (FDR) correction, which was applied to each category of features (base, IMU-based, fatigue) separately.
In total, 76 PwMS and 25 HC were enrolled of which 67 PwMS and 18 HC were included in the analyses presented here. The baseline demographics and disease characteristics of the participants included here (see Table 1, annexed to the end of the description of this patent application) were similar to those of the full study population.16 With a mean (SD) EDSS score of 2.4 (1.4) and a mean (SD) 9HPT time of 22.3 (4.2) seconds for both hands and 22.1 (4.7) for the dominant hand, PwMS included in the analyses had mostly mild disease with limited upper extremity functional impairment. Compared with HC, the PwMS cohort had a larger proportion of female participants (68% vs 33%) and was on average slightly older (39.2 [7.8] years vs 35.0 [8.9] years).
The test-retest reliability analysis for PwMS is presented in
Most base features showed either fair or moderate-to-good correlations with the standard clinical measures of upper extremity function and overall disease severity as shown in
Next, a partial correlation analysis was conducted on the base features to assess whether they are primarily driven by an upper extremity function and cognitive component, see
The ability of the Pinching Test features to differentiate and distinguish between HC, PwMS-Normal and PwMS-Abnormal is summarized in Table 2, annexed to the description of this application. Overall, base features demonstrated the greatest ability to differentiate between PwMS-Normal and PwMS-Abnormal. For the eight base features that showed a statistical significant difference between the two subgroups (green table cells; p<0.05)—which included the number of successful pinches—AUC ranged from 0.68-0.79 and Cohen's d from 0.43-1.04. Additionally, three base features differentiated between HC and PwMS-Abnormal (AUC=0.75-0.75; Cohen's d=0.35-0.79; all p<0.05 for all three features). Some fatigue features differentiated between PwMS-Normal and PwMS-Abnormal when using the standard deviation aggregation method instead. For the five fatigue features that showed a statistically significant difference between these two subgroups (green table cells; p<0.05), AUC ranged from 0.70-0.82 and Cohen's d from 0.38-1.10. In addition, three of these features also differentiated between PwMS-Abnormal and HC (AUC: 0.74-0.76; Cohen's d=0.52-0.64; p<0.05 for all three features).
The relationship between the base features was also studied. The pairwise Spearman's rank correlation analysis revealed that several features were independent of each other, as shown in
Reliable features from the simple and ecologically valid smartphone sensor-based Pinching Test were associated with standard clinical measures of upper extremity function, overall disease severity, cognitive function, and fatigue; and identified PwMS with upper extremity function impairment. The Pinching Test is designed to measure the ability to perform daily life activities such as grasping objects, buttoning a shirt, or controlling eating utensils, and can be frequently and independently performed by the patient at home. By taking advantage of sensors embedded in commercially available smartphones, it enables the assessment of multiple aspects of pinching, including the accuracy, efficiency, and smoothness of pinching as well as the range of motion and coordination of two fingers. Ideal Pinching Test features fulfill the following three criteria, among others: test-retest reliability, agreement with standard clinical measures, and ability to differentiate and distinguish between PwMS with and without upper extremity functional impairment. In this study, we identified features that fulfill all three criteria, as shown in Table 3 (annexed to the end of this application). These include a majority of the base features, including number of performed pinches, number of successful pinches, two-finger attempts fraction, pinch time, gap time, double touch asynchrony, last points distance, and finger path ratio. These features demonstrated moderate or good test-retest reliability, which is in line with previous studies on smartphone sensor-based assessments of upper extremity function in MS25 and Parkinson's disease26, 27. The most reliable Pinching Test features achieved an ICC(2,1) of 0.81 that is compared to the ICC of 0.84 previously reported for the 9HPT,28. Agreement with both the clinician-reported assessments (9HPT and EDSS) and patient's perspective of the impact of the disease (arm-related items of the MSIS-29) was generally strongest for base features. Since pinching as many tomatoes as possible within 30 seconds on a smartphone device requires motor skills, but also fast information processing and attention, it is not surprising that many base features correlated with both the 9HPT and oral SDMT. Additionally, both clinical measures are confounded by disease severity. Hence, some degree of correlation with the oral SDMT can be expected. Consequently, features that primarily capture a motor component would retain a significant correlation with 9HPT even after accounting for oral SDMT, while the correlation with oral SDMT would tend to zero after accounting for 9HPT. Number of performed pinches, double touch asynchrony, finger path ratio, and gap time all showed this characteristic. It is not surprising that double touch asynchrony was identified as feature primarily driven by a motor component as it measures the duration between the thumb and the second or third finger touching the smartphone screen at the beginning of the pinching gesture. As such, it was designed to be independent from cognitive tasks involved in recognizing a new tomato shape appearing on the screen. Base features also showed the greatest ability to differentiate between PwMS-Normal and PwMS-Abnormal, indicating that greater levels of functional impairment resulted in poorer performance on the Pinching Test. Three of these features—two-finger attempts fraction, gap time and double touch asynchrony—also differentiated between HC and PwMS-Abnormal. A global 9HPT threshold was used to classify PwMS as either PwMS-Normal or PwMS-Abnormal. This threshold was derived from the normative population of Erasmus et al.23 as this population shows a similar age and sex distribution as our PwMS cohort. The small number of HC and PwMS-Abnormal and the imbalance in age and sex between the groups limited the ability to differentiate HC from PwMS subgroups.
IMU-based features, which assess the function of the hand holding the smartphone, generally fulfilled the test-retest reliability criterion, see Table 3, annexed to the description. ICC(2,1) was compared to those obtained with the base features, but their performance in terms of their agreement with clinical measures and their ability to differentiate between HC and PwMS subgroups was poorer. Considering that most PwMS enrolled in this study had relapsing-remitting disease, it is possible that the amplitude of movement abnormalities or motor deficits encountered in this study were not large enough for these features to capture disease-related signals. Lastly, we investigated the fatigue features. These features compare the test performance during the first with the second half of the test, and we hypothesized that they would capture fatigue. A few fatigue features fulfilled up to two of the three criteria (fatigue pinch time fulfilled all three criteria), when aggregating individual tests by taking the standard deviation, as shown in Table 4. The improved performance of the standard deviation aggregation may reflect fluctuations in fatigue, as it is commonly observed in PwMS.20
The Pinching Test offers an objective, self-administered assessment of upper extremity function, which can complement the standard clinical evaluation of MS. A range of features were investigated and it was possible to identify those that provide reliable measures of various aspects of the pinching motion, including: accuracy, efficiency, responsiveness and smoothness of pinching; agreement with clinical measures of upper extremity function and overall disease severity; and ability to differentiate between PwMS with and without upper extremity functional impairment.
| Number | Date | Country | Kind |
|---|---|---|---|
| 21166119.4 | Mar 2021 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/058486 | 3/30/2022 | WO |
