Patent Application
20240268671
This disclosure relates generally to virtual or remote care in medicine, and more specifically remote or augmented physical examination of a patient using a medical palpation device held in the patient's hand and used by the patient at a clinician's direction to palpate a patient's body during a virtual physical examination.
Abdominal pain is the most common reason for unscheduled medical care. According to the American Hospital Association, there are over 130 million emergency department (ED) visits per year. See American Hospital Association, Fact Sheet: Telehealth, February 2019. Eleven million ED visits have a principal reason of stomach and abdominal pain, cramps, and spasms. Of those, six million receive a primary diagnosis in the abdominal pain group. See Reed M E, Parikh R, Huang J, et al., Real-Time Patient-Provider Video Telemedicine Integrated with Clinical Care, N Engl J Med. 2018; 379:1478-1479.
The Centers for Medicare & Medicaid Services has produced guidelines for documenting in-person physical exam guidelines by twelve organ systems or seven body areas. See Centers for Medicare & Medicaid Services, 1995 Documentation Guidelines for Evaluation and Management Services, and 1997 Documentation Guidelines for Evaluation and Management Services.
There are an extensive number of diagnoses associated with abdominal pain. Palpating the abdomen enables a clinician to determine if there is a chance of “surgical abdomen,” which is pathology that may need surgical intervention, such as appendicitis, small bowel obstruction, intestinal perforation, hernia, etc. When a clinician palpates the abdomen of a patient and based on their palpation, suspects that the patient may have a surgical abdomen, the patient is then further evaluated with advanced imaging: computed tomography scan (CT), ultrasound (US), or magnetic resonance imaging (MRI). Occasionally, the patient will be admitted as an inpatient for repeat abdominal examinations or taken directly to the operating room for surgical management without advanced imaging.
Ten million total CT scans are performed on the abdomen or pelvis annually. See Reed et al. Furthermore, although CT use is common in patients that present to the ED with abdominal pain (˜40% for adults, ˜15% for pediatric), CT use is linked with some significant risk, including higher rates of cancer. See Centers for Medicare & Medicaid Services, 1995 and 1997 Documentation Guidelines for Evaluation and Management Services.
Telehealth has been an increasingly important aspect of the delivery of healthcare. According to the American Hospital Association, telehealth has the potential to reduce costs and improve access to care, especially for underserved populations and residents of rural areas. The American Hospital Association fact sheet reports that 79% of hospitals in the United States use some form of telehealth. Despite the growth in telehealth, abdominal pain generally cannot be safely and accurately evaluated virtually because the clinician cannot palpate the abdomen to determine if a patient needs advanced imaging or surgery.
For telehealth to be maximally beneficial, patients need to have as much of the tasks performed at an in-person physician visit done virtually. A complete Virtual Physical Exam (VPE) allows a clinician to conduct an exam remotely. In order for a clinician to perform a physical examination effectively in a telehealth visit, a VPE should provide real-time, high-quality information similar to an in-person visit. In addition to reliance on videoconferencing applications for telehealth visits, episodic use from inexpensive devices may be needed. In particular, a virtual physical examination should include objective data from and needs to enable the following tasks:
Notably, palpation in abdominal exams continues to lack an alternative to patient-reported and subjective descriptions of the patient's experience with self-examination and palpation. This may contribute to gastroenterology ranking as the second lowest internal medicine specialty to incorporate telemedicine per the American Medical Association's Patient Practice Benchmark Survey. Thus, while telehealth does enable clinicians to care for many clinical conditions remotely, it does not readily allow for the diagnosis and management of ailments such as acute abdominal pain where the clinician needs to ascertain if the patient has a “surgical abdomen” and needs advanced imaging, lymph node concerns, traumatic injuries, or musculoskeletal complaints, all of which generally require the ability to palpate the abdomen or other area of injury/pain.
A device, system, and method to perform standardized remote home palpation will enable safe virtual care of abdominal pain patients and provide an avenue for health care providers to increase the scope of pathologies that may be safely and accurately assessed via telemedicine.
Embodiments of a physical examination device, system and method disclosed herein enable tele-palpation of a patient by standardizing home palpation in order to enable safe virtual care of abdominal pain patients. The device enables a patient or caregiver to palpate (press) on the patient's abdomen or area of concern and provide sensor pressure data to a software product connected to the device during palpation of the abdomen. Embodiments of a software product disclosed herein analyze the sensor pressure data and provide usable data to a clinician regarding risk of the patient's abdominal pain, including information regarding whether advanced imaging is likely needed. The clinician may then incorporate the device's findings into the clinician's decision-making about the likelihood of pathology (appendicitis, a broken bone, etc.).
In one embodiment, a handheld device is intended for use by patients during virtual abdominal exams and other virtual physical exams where palpation is needed. Embodiments of the disclosed palpation device include a non-sterile, reusable medical device that can provide data and information to a remote healthcare provider to help determine whether abdominal imaging or further in-patient treatment in the ED is needed.
In some embodiments, the device is disclosed as comprising a handle, a palpation head, a digital force sensor and processing circuit, a compression member comprising a comfort spring or rigid block, a rechargeable battery pack, a Bluetooth module, and a USB port. During use, the patient or caregiver takes the device and pushes on their sternum as hard as they can or until they experience any discomfort to establish a baseline. The device measures the maximum force applied and transmits it via Bluetooth to a connected computer or smartphone. The connected computer or smartphone has a software application that receives the force data, applies experimentally derived algorithms to the data, and then informs the clinician of the extent to which the patient has clinically significant tenderness. The clinician can then integrate this information with the history and other physical exam findings to arrive at a diagnosis.
While the invention is described with reference to the above drawings, the drawings are intended to be illustrative, and other embodiments are consistent with the spirit, and within the scope, of the invention.
The various embodiments now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific examples of practicing the embodiments. This specification may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this specification will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Among other things, this specification may be embodied as methods or devices. Accordingly, any of the various embodiments herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. The following specification is, therefore, not to be taken in a limiting sense.
A second end of the palpation device comprises an opening (115, 215 as shown in
Lock system 240 in
Sensor internal chip 250 as shown in
In some embodiments, the voltage divider circuit 260 comprises an analog circuit module that processes the external force applied by the user. Voltage divider circuit 260 may have a programmable reference voltage for sensitivity adjustment, and a connection to pressure pad 250 (e.g., a force sensitive resistor). In one embodiment, pressure pad 250 comprises a force sensitive resistor A201-25 pad manufactured by Tekscan, Inc. under the trade name Flexiforce™. In one embodiment, a voltage divider circuit 260 may comprise a voltage divider circuit module also manufactured by Tekscan, Inc. Voltage divider circuit 260 may have a programmable reference voltage to set a default sensitivity adjustment to calibrate the force sensor. In other embodiments, an inverting op-amp circuit module or a non-inverting op-amp circuit module (not shown) may be used in place of voltage divider circuit module 260. In some embodiments, internal space 212 of handle 210 in
In some embodiments, cord connection 280 comprises a cord sheath that protects the wires from damage inside the handle, and a power cord to connect the palpation device to a power adapter or charger, and/or a user device such as mobile phone, tablet or laptop. In some embodiments, cord connection 280 is connected to power source 270 which may comprise a rechargeable battery known in the art such as a lithium-ion battery. In one embodiment, power source 270 may also include a power transfer module comprising a small chip that enables transfer of power and data from the palpation device to a user device such as a mobile phone, tablet or laptop or desktop computer. Cord connection 280 may also comprise a USB-C port in one embodiment, which allows for recharging of the palpation device, powering the device from an external charger, and direct connection to a computer for data transfer. Cord connection 280 may also comprise a power cord that is connected to one or more connector heads 285 that may comprise one or more connectors such as a micro-USB, Lightning port connector, or USB-C port connector that may connect to a user's mobile phone, tablet, laptop computer, or other networked device.
Finally, in the embodiment shown in
In some embodiments, power port 580 is connected to an internal power source 570 for powering pressure pad 530, sensor internal chip 550, and Bluetooth internal chip 560. Power source 570 may in one embodiment comprise a rechargeable battery known in the art such as a lithium-ion battery. Power port 580 may further comprise a data port, and/or a power cord connection that includes a USB-C port in one embodiment, which allows for recharging of the palpation device, powering the device from an external charger, and/or direct connection to a computer for data transfer. Power port 280 may also comprise a power cord that is connected to one or more connector heads 285 that may comprise one or more connectors such as a micro-USB, Lightning port connector, or USB-C port connector that may connect to a power source such as an electrical outlet or power bank, or a user's mobile phone, tablet, laptop computer, or other networked device.
In the embodiment shown in
Embodiments of the medical palpation devices shown in
Once these preliminary steps have been completed, in some embodiments of the present disclosure a web or mobile application launches on the networked device (e.g., smart phone, tablet, or laptop/desktop computer). After the patient clicks “Accept”, a demonstration sequence guides the patient or caregiver to follow the directions in the user demonstration sequence for palpating the patient's body. A patient may perform the sequence on his or her own body using the palpation device for self-palpation. Alternatively, a caregiver may perform the palpation sequence by applying the palpation device to a patient's body. In one embodiment, the demonstration sequence guides the patient or caregiver to palpate a patient's abdomen. In screenshot 700A shown in
First, in screenshot 700B shown in
Next, the patient or user is requested to identify the four abdomen quadrants to be palpated: Left Upper Quadrant (LUQ), Left Lower Quadrant (LLQ), Right Lower Quadrant (RLQ), and Right Upper Quadrant (RUQ). In screenshot 700C shown in
Next, the patient or user is instructed to palpate the Right Upper Quadrant (RUQ) of the patient's abdomen as hard as possible or until it hurts, as shown in screenshot 700F of
It is expected that the patient may have a mechanism to begin measurements and stop measurements when the patient indicates that they are finished pushing on the designated location in each palpation. In some embodiments, the medical palpation device produces five data files for the patient containing pressure sensor data from each of the 5 palpations performed on the patient as instructed by the software. Sensor data may be collected multiple times at regular intervals (e.g. 50 times/second) during the palpation. In one embodiment, for each of the five sensor data files, the average of the top 25 maximal pressure readings are used as discussed below. This produces one pressure reading for each of the five palpation locations on the patient's body. For each of the four abdominal quadrants (LUQ, RUQ, LLQ, RLQ), in one embodiment the difference between the pressure reading and the baseline sternum pressure reading is used as the maximum reliable pressure for that quadrant. This data is then transmitted from the palpation device to the patient's mobile device, tablet, and/or laptop or desktop computer for further analysis.
In one example use case of embodiments of the medical palpation device and data interpretation software discussed herein, a 27-year-old woman is experiencing abdominal pain and would like to have a medical assessment. She contacts her physician's office, who arranges a virtual visit. Before the virtual visit, the woman uses other devices to assess her vital signs (temperature, heart rate, respiratory rate, blood pressure, and pulse oxygen saturation) and provide recordings for physician review of her heart, lungs, and abdomen. She then uses the medical palpation device discussed herein with respect to various embodiments to push on the four quadrants of her abdomen, which provides the data to the physician to use during the encounter. The physician starts the virtual visit with the woman. Based on the woman's history and the device's indication that she has clinically significant tenderness in the right lower quadrant, the physician orders a same-day ultrasound of the woman's appendix to be performed to evaluate for appendicitis.
In another example use case of embodiments of the medical palpation device and data interpretation software discussed herein, a 30-year-old man is experiencing nausea and vomiting and would like to have a medical assessment. He logs into virtual urgent care. Before the virtual visit, the man uses other devices to assess her vital signs (temperature, heart rate, respiratory rate, blood pressure, and pulse oxygen saturation) and provide recordings for physician review of his heart, lungs, and abdomen. He then uses this invention to push on the four quadrants of his abdomen, which provides the data to the physician to use during the encounter. The physician starts the virtual visit with the man. Based on the man's history and the device's indication that there is no clinically significant tenderness in the abdomen, the physician recommends an antiemetic, water and Gatorade for likely gastroenteritis.
In operation 820, a set of pressure readings is received from the handheld palpation device for each of a plurality of locations on the user's body to identify a maximum reliable pressure for each abdominal quadrant as follows. In some embodiments of the invention, operation 820 may be performed in accordance with Step 1 below. Operation 830 may be performed in accordance with Step 2 below. Operation 840 may be performed in accordance with Step 3 listed below. Operation 850 may be performed in accordance with Step 4 listed below. Next, operation 860 may be performed in accordance with Step 5 listed below.
Step 1: Ingest sensor readings for each of the five locations:
Step 2: For each location sensor reading, create maximum pressure by:
Step 3: For each maximum pressure in the abdomen, create a maximum reliable pressure as follows:
Step 4: Multiply the maximum reliable pressure for each of the features and the appropriate weighting coefficient:
Step 5: Combine the above into the predicted score and calculate a probability of needing advanced imaging or hospitalization.
In some embodiments of the invention, a probability of needing advanced imaging or hospitalization is computed using machine learning techniques. In one embodiment an algorithm is developed statistically from approximately 76 patients presenting to a regional medical center (Arrowhead Regional Medical Center (ARMC)) emergency department with a chief complaint of abdominal pain, nausea, vomiting and/or diarrhea. Additional and/or other data may be collected for a training or feasibility set for use in embodiments of the present invention. In some embodiments, a linear regression model is used. Other machine learning models known in the art may also be contemplated in other embodiments. In one embodiment, multiple logistic regression is performed with the outcome variable of the patient being whether the patient requires advanced imaging or admission, and the predictor variables being the maximum reliable pressure on each quadrant with all interaction effects. This produces a predicted probability for admission or advanced imaging, and this is placed into three categories of likelihood: low, indeterminate, and high.
An example implementation in some embodiments is provided below for a representative training data set including a patient sample from a county hospital emergency department as described above.
In one embodiment, each patient in a derivation study had basic data of each patient that consented to participate in the study entered into an Excel spreadsheet: age, birth gender, race ethnicity, height, weight, ED diagnosis, duration of symptoms, disposition, abdominal pain location, final diagnosis, whether or not they required immediate surgical intervention, whether a CT scan was done, and whether an ultrasound was done. Additionally, the palpation device produced five files for each patient containing sensor data. For each of the five sensor data files, the average of the top 25 maximal pressure readings were used. This produced one pressure reading for each of the five locations for the patient, and for the for abdominal quadrant, the difference between that pressure reading and the baseline sternum pressure reading was used as the maximum reliable pressure for that quadrant. Multiple logistic regression was then performed with the outcome variable of the patient required advanced imaging or admission, and the predictor variables where the maximum reliable pressure for each quadrant with all interaction effects. This produced a predicted probability for admission or advanced imaging, and this was placed into three categories of likelihood: low, medium, and high. In one embodiment, the linear regression analyses were performed using Stata 16.1 MP (StataCorp, College Station, TX).
In one embodiment of a training set, out of 83 patients who consented to participate in the study, there was complete sensor, imaging, and disposition data on 76 patients for our study sample. In this sample of patients that made it back to a bed in a busy County emergency department, there was a very high rate of imaging (86%), admission (42%), and surgical intervention (15%) (table 1). The multivariate logistic regression including the maximum reliable pressure data for each quadrant on the patient that was normalized by the maximum reliable pressure data for the sternum, demonstrated strong modeling characteristics in this derivation study. All interaction terms were included in the model based on an a priori hypothesis and were therefore included in the final model to reduce over fitting the data, although some of the individual interaction terms were not statistically significant (table 2). The model demonstrated a likelihood ratio chi-squared with 15° of freedom of 28.83, a P value of 0.017, and a pseudo-R squared of 0.49. This produced an area under the curve of 0.95.
The purpose of the device is for a provider who is not physically present with the patient to determine whether or not the patient can be safely treated virtually for their abdominal pain. To make this most clinically useful, three categories of risk were created for the patient needing advanced imaging or hospital admission: low, medium, and high. Based on informal clinician feedback, a low risk group was created where the predicted probability was less than 10%, a high risk from group was created where the predicted probability was greater than 90%, and a medium or indeterminate risk group that fell between the two cutoffs. This allows evaluation of the device performance and its intended clinical use, albeit with a derivation set in this case. The performance of the device in this derivation sample was very good without any miscategorizations in either the low or the high risk groups (Table 3).
In an initial feasibility study as described above, complete data was collected on 76 patients presenting to a large county hospital emergency department with chief complaint of abdominal-related complaints: pain, nausea, vomiting and/or diarrhea. Patients with abdominal pain who need unscheduled care may choose to use telehealth if it were possible to care for abdominal pain virtually. Currently, these patients must choose between an urgent care or an emergency department to get their care, so a sample of ED patients with abdominal pain may be a reasonable representation of patients that could benefit from virtual care alternatives for which embodiments of the disclosed palpation device may be employed. In this sample, patients were asked to use the device on themselves. Separately, a blinded physician would evaluate the patient as per the standard of care, including palpation of the abdomen. In this sample, the device was able to correctly categorize patients into low, indeterminate (medium), and high-risk groups for the physician ordering advanced imaging or hospital admission. 100% (53/53) of the patients that the device predicted as high risk required imaging and/or admission, which suggests the device is very effective at detecting high acuity pathologies. 13/21 patients (61.9%) in the indeterminate-risk group underwent imaging and/or admission. Indeterminate risk readings were, therefore, unable to predict the need for imaging/admission reliably. None of the patients (0/2) deemed as low risk required imaging and/or admission.
In some embodiments, the device is an aid in assessing the likelihood that a patient with abdominal pain being assessed virtually through telehealth requires in-person imaging or hospital admission. Thus, there may be two types of errors that occur with use of the device: falsely recommending an in-person visit when it is not needed (i.e., a false positive), or falsely recommending virtual care when an in-person visit is needed (i.e., a false negative).
If the device unnecessarily recommends an in-person visit, the patient will receive the current treatment standard and not be exposed to potential risk to health. The patient may experience some lost time and/or temporary anxiety until they are seen in-person by a healthcare provider. Upon in-person examination, a physician would palpate the patient's abdomen and if needed, order imaging or admit the patient. Thus, a false positive result would not present an increased risk to health over the current standard of care.
On the other hand, if the device indicates that a patient is at low or indeterminate risk when the patient has a surgical abdomen, there is the potential for a delay in treatment. This risk will be mitigated by the labeling of the device, which will indicate that the results from the device must be assessed together with a patient's clinical signs and symptoms and the device results should never be used to override clinical judgment if a patient's signs and symptoms indicate that a patient should be seen in-person for imaging or hospital admission. Further, patients are currently being assessed virtually for abdominal pain. This assessment relies entirely on qualitative self-reports from patients. In some embodiments, the proposed device can provide additional information beyond what virtual providers have today. Thus, the risk of a potential false negative is no greater than the potentially unreliable self-report from a patient that is provided today via telehealth. The risk of potential false positives and false negative results through use of embodiments of the present invention can be appropriately mitigated through additional bench, usability, and clinical studies.
From a technological perspective, the act of using embodiments of the disclosed the palpation device itself, does not have the potential to cause a risk to health. Just as if a physician were pushing on a patient's belly, the pressing will only occur up to the point of the patient experiencing pain provided the patient and/or user performs the palpations using the palpation device as instructed. Thus, from a technological perspective, the risks to health are low and can be addressed through appropriate verification and validation testing, including software, electrical safety, and electromagnetic compatibility testing.
Embodiments of the current invention disclose methods that enable a range of uses including a fully remote use case with a patient using the palpation device, and a remote care provider via telehealth. If there is no escalation in care needed (e.g., admission to the ER or advanced imaging), the patient care could also be fully self-service. Other care locations may also be contemplated in some embodiments discussed herein, including pharmacies, clinics, hospitals, or even schools, work sites, etc. Potential users could be students, customers or employees doing self-palpation, or palpation with a care provider such as a physician (MD), a registered nurse (RN), an nurse practitioner (NP), a pharmacy technician, or a school nurse, but are by no means limited by the examples above.
The value proposition to the patient of embodiments of the present disclosure include not having to make unnecessary trips to the emergency department (ED), and reduced cost through receiving the correct level of care.
The value proposition to insurance companies of embodiments of the present disclosure include faster and more accurate triage, avoiding imaging and unnecessary surgery costs, while improving patient outcomes, including reduced hospital and ED admissions, readmissions, and emergency surgeries.
The value proposition to employers of embodiments of the present disclosure could include reducing missed work days by employees, and reduced care costs (especially for self-insured employers).
Finally, the value proposition to physicians and care providers of embodiments of the present disclosure include an increase in workflow throughput, better care metrics and results, and reduced liability to patients.
In screenshot diagram 900B, an exemplary screen of a smart phone or other mobile device of a clinician or physician is shown depicting the palpation results for a 34 year old female patient, Jane Doe, who has just completed the self-palpation process. The software analytics process has been completed and a risk of acute abdomen is given as “LOW” meaning that depending on the patient's history, the clinician does not need to advise the patient to go to the ED and/or order further imaging tests. In screenshot diagram 900C, an exemplary screen of a smart phone or other mobile device of a clinician or physician is shown depicting the palpation results for a 49 year old female patient, Sally Jones, who has just completed the self-palpation process. The software analytics process has been completed and a risk of acute abdomen is given as “Indeterminate”, and a further note was included that there was some tenderness in the Right Lower Quadrant of the abdomen. At this point the clinician may review the patient's history more closely or conduct further examination via telehealth, and then depending if further concerns are raised, the clinician may advise the patient to go to the ED and/or order further imaging tests.
The electronically readable medium may be any non-transitory medium that stores information electronically and may be accessed locally or remotely, for example via a network connection. In alternative embodiments, the medium may be transitory. The medium may include a plurality of geographically dispersed media each configured to store different parts of the executable code at different locations and/or at different times. The executable instruction code in an electronically readable medium directs the illustrated computer system 1000 to carry out various exemplary tasks described herein. The executable code for directing the carrying out of tasks described herein would be typically realized in software. However, it will be appreciated by those skilled in the art, that computers or other electronic devices might utilize code realized in hardware to perform many or all the identified tasks without departing from the present invention. Those skilled in the art will understand that many variations on executable code may be found that implement exemplary methods within the spirit and the scope of the present invention.
The code or a copy of the code contained in computer program product 1060 may reside in one or more storage persistent media (not separately shown) communicatively coupled to system 1000 for loading and storage in persistent storage device 1070 and/or memory 1010 for execution by processor 1020. Computer system 1000 also includes I/O subsystem 1030 and peripheral devices 1040. I/O subsystem 1030, peripheral devices 1040, processor 1020, memory 1010, and persistent storage device 1070 are coupled via bus 1050. Like persistent storage device 1070 and any other persistent storage that might contain computer program product 1060, memory 1010 is a non-transitory media (even if implemented as a typical volatile computer memory device). Moreover, those skilled in the art will appreciate that in addition to storing computer program product 1060 for carrying out processing described herein, memory 1010 and/or persistent storage device 1070 may be configured to store the various data elements referenced and illustrated herein.
Those skilled in the art will appreciate computer system 1000 illustrates just one example of a system in which a computer program product in accordance with an embodiment of the present invention may be implemented. To cite but one example of an alternative embodiment, execution of instructions contained in a computer program product in accordance with an embodiment of the present invention may be distributed over multiple computers, such as, for example, over the computers of a distributed computing network.
Instructions for implementing a machine learning-based propensity score model, a logistic regression model, a probit model, and/or an artificial neural network implementing any of the above in accordance with disclosed embodiments may reside in computer program product 1060. When processor 1020 is executing the instructions of computer program product 1060, the instructions, or a portion thereof, are typically loaded into working memory 1010 from which the instructions are readily accessed by processor 1020.
In one embodiment, processor 1020 in fact comprises multiple processors which may comprise additional working memories (additional processors and memories not individually illustrated) including one or more graphics processing units (GPUs) comprising at least thousands of arithmetic logic units supporting parallel computations on a large scale. GPUs are often utilized in deep learning applications because they can perform the relevant processing tasks more efficiently than can typical general-purpose processors (CPUs). Other embodiments comprise one or more specialized processing units comprising systolic arrays and/or other hardware arrangements that support efficient parallel processing. In some embodiments, such specialized hardware works in conjunction with a CPU and/or GPU to carry out the various processing described herein. In some embodiments, such specialized hardware comprises application specific integrated circuits and the like (which may refer to a portion of an integrated circuit that is application-specific), field programmable gate arrays and the like, or combinations thereof. In some embodiments, however, a processor such as processor 1020 may be implemented as one or more general purpose processors (preferably having multiple cores) without necessarily departing from the spirit and scope of the present invention.
While the present invention has been particularly described with respect to the illustrated embodiments, it will be appreciated that various alterations, modifications, and adaptations may be made based on the present disclosure and are intended to be within the scope of the present invention. While the invention has been described in connection with what are presently considered to be the most practical and preferred embodiments, it is to be understood that the present invention is not limited to the disclosed embodiments but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the underlying principles of the invention as described by the various embodiments referenced above and below.
This application claims the benefit of U.S. Provisional Application No. 63/445,997, filed on Feb. 15, 2023. The entire content of these application is hereby incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63445997 | Feb 2023 | US |
