Patent Application
20230200625
Field of the Invention: Embodiments of the invention relate to systems, devices, and methods to assist surgical procedures, particularly using Artificial Intelligence (AI).
In recent years, Artificial Intelligence has begun to be developed to be used to process images to recognize features of a human face as well as different anatomical structures in a human body. These AI tools can be used to automatically recognize an anatomical feature to assist an operator during a medical procedure. Computational methods such as machine learning and deep learning algorithms can be used for image or language processing to gather and process information generated in a medical procedure. The hope is to use AI algorithms that can then be used to or improve the outcome of the surgery. Current AI-assisted surgical systems and methods are still less than ideal in many respects to be used to, for example, guide a surgical procedure. Accordingly, improved AI-assisted surgical systems and methods are desired.
Various embodiments of the invention relate to computer-implemented medical systems, devices, and methods to guide a surgical procedure such as by identifying and labelling anatomical features in real-time and placing one or more landmarks on the identified anatomical feature. Surgeons use physical landmarks to underpin a variety of cognitive tasks; keeping track of latent vascularity, staple lines, suture locations, latent anatomical structures, etc. The landmarks are typically placed using dyes, cauterization marks, etc. In some embodiments, needles are inserted from the outside to mark points. Placing a physical landmark which may require implanting an object in the patient's body may add to complications of the surgery and physically inhibit movement of surgical tools in the course of surgery. Other issues may involve a mistake made by an operator in the course of a surgery which can be costly. For example, it may be difficult or impossible for an operator to know the exact location of a critical anatomical feature that is hidden from a camera (e.g., a camera used during an arthroscopic or endoscopic surgery), or a change of field of view may make it difficult for the operator to identify the location of a landmark. Therefore, computer-implemented medical systems, devices, and methods such as Artificial Intelligence (AI) tools, particularly for guiding medical procedures by applying a virtual landmark on the patient's body (e.g., on an organ or anatomical feature) can be valuable. These AI tools can have their limitations in accurately and reliably predicting a tool, anatomical structure, or detecting a procedure. In a fast-paced surgical procedure, the AI tool may need to also make predictions with low latency to provide real time assistance to an operator.
Recognized herein is the need for fast, accurate and reliable AI tools to assist an operator in real time during the course of a surgical operation or other medical procedure by placing a virtual landmark on a location of interest to facilitate the surgical (or other medical) procedure for the operator (e.g., surgeon, interventional radiologist) and to improve an outcome of the surgery or other medical procedure. Accordingly, various aspects and embodiments of the present invention provide a pipeline of machine learning algorithms that is versatile and well trained for unique needs of landmarks in various medical procedures.
Various embodiments of the invention described herein provides systems, devices, and methods that can receive information (e.g., image, voice, user inputs) prior to and during a medical procedure (e.g., a surgery), process the received information to identify features associated with placing a landmark associated with the procedure, and place a virtual landmark at a location of interest in real time during the procedure.
Aspects of the present invention also aid surgeons to place a landmark on a location of interest intraoperatively by using images acquired preoperatively using imaging modalities and associated methods such as fluoroscopy, magnetic resonance imaging (MRI), or computed tomography (CT) scanning. In one or more embodiments, the preoperative images can be that of a surgical field of view and Artificial Intelligence (AI) can be applied to preoperatively generated images to overlay the images and/or location of a landmark onto a real-time video stream of a surgical procedure to provide guidance to a surgeon. We refer to AI modules/algorithms used intraoperatively, preoperatively, or postoperatively to assist with the surgical procedure or improve an outcome of the procedure as Surgical AI.
One aspect of the present invention provides systems for assisting an arthroscopic procedure such as a repair to a shoulder, knee, hip, ankle or other joint by allowing computer-implemented arbitrary landmark placement, the system comprising one or more computer processors and one or more non-transitory computer-readable storage media storing instructions that are operable, when executed by the one or more computer processors, to cause the one or more computer processors to perform operations comprising: receiving a video stream from an arthroscopic imaging device; receiving one or more sets of coordinates of one or more landmarks; overlaying the one or more landmarks on the video stream; and displaying the overlay on one or more display devices intraoperatively to be used by an operator during the arthroscopic procedure. Application of embodiments of the system to the assistance to other medical procedures (e.g., by the placement of arbitrary landmarks) including minimally invasive procedures such as endoscopic, laparoscopic, and interventional cardiovascular procedures is also contemplated. Examples of such minimally invasive procedures can include one or more of Gastro-intestinal (GI) procedures (e.g., biopsy of the intestines, removal of polyps, bariatric surgery, stomach stapling/vertical banded gastroplasty), urological procedures (e.g., removal of kidney stone, bladder repair), gynecological procedures (e.g., a dnc, removal of uterine fibroids) and laparoscopic procedures (e.g., an appendectomy, cholecystectomy, colectomy, hernia repair, nissen fundoplication).
In some embodiments, the operations further comprise identifying and labeling one or more elements in the video stream using at least one of a trained computer algorithm. In some embodiments, the one or more elements comprise one or more of an anatomical structure, a surgical tool, an operational procedure or action, or a pathology. In some embodiments, the identifying and labeling the one or more elements in the video stream comprises using one or more software modules (herein modules). In some embodiments, the one or more modules may comprise modules for performing video stream decomposition, tool recognition, anatomy recognition, tool tracking, gesture recognition, landmark point registration, or anatomy and landmark tracking. In some embodiments, the system recommends one or more landmarks based at least partially on the identified elements.
In some embodiments, the operations further comprise: storing the one or more sets of coordinates of one or more landmarks; changing a view of the display to omit the overlaid landmark from being displayed; reverting the view to the previous display; identifying the one or more set of coordinates for the one or more landmarks; and re-overlaying the one or more landmarks. In some embodiments, the operator activates the changing and the reverting steps. In some embodiments, changing a view step is activated automatically based on a change in an identified anatomical structure or pathology.
In some embodiments, the one or more sets of coordinates of the one or more landmarks is provided by an operator (e.g., a surgeon, interventional cardiologist, radiologists, etc.) intraoperatively. In some embodiments, the one or more sets of coordinates of the one or more landmarks is provided by an operator preoperatively. In some embodiments, the one or more sets of coordinates of the one or more landmarks is generated from one or more medical images of a subject. In some embodiments, the one or more medical images are radiological images of the subject. In some embodiments, the radiological images are from a joint other boney structure of the subject. In some embodiments, the radiological images are associated with a shoulder, a knee, a hip, ankle or elbow of the subject. In some embodiments, the radiological images are generated using fluoroscopy, magnetic resonance imaging (MRI), computed tomography (CT) scanning, positron emission tomography (PET) scanning or ultrasound imaging.
In some embodiments, the video stream is provided by an arthroscope (or other imaging device) during the arthroscopic procedure. In various embodiments, the arthroscopic procedure may correspond to one or more of the following types of procedures (for which embodiments of the systems and modules may be so configured for assisting with): ACL repair in a knee surgery; graft placement procedure, e.g., that used in a superior capsule reconstruction of a torn rotator cuff; a decompression procedure; a removal of or a resection of one or more inflamed tissues; removal of or a resection of one or more frayed tendons where the video stream is monocular. In one or more of the above and other procedures the video stream may be stereoscopic or monocular unless otherwise noted in the specific procedure. Also in various implementations embodiments of the systems of the invention can be configured to toggle or switch back and forth between monocular or stereoscopic inputted video stream and associated outputted video overlays.
In some embodiments, the one or more computer processors receive the video stream from one or more camera control units using a wired media connection. In some embodiments, a latency between receiving the input from the digital camera and overlay the output and the videos stream is at most 40 milliseconds (ms) to accommodate a digital camera with about 24 frames per second (fps). In some embodiments, the latency between receiving the input from the digital camera and overlay the output and the videos stream is no more than a time between two consecutive frames from the digital camera.
In some embodiments, the one or more computer processors receive the video stream from one or more camera control units using a network connection. In some embodiments, the interventional imaging device is a digital camera specialized for arthroscopic use. In some embodiments, the digital camera is mounted on a rigid scope, suitable for work in the arthroscopic joints. In some embodiments, the camera control unit is configured to control a light source, capture digital information produced by the digital camera. In some embodiments, the camera control unit converts the digital information produced by the digital camera into the video stream. In some embodiments, the camera control unit record the digital information produced by the digital camera in a memory device. In some embodiments, the memory device is a local memory device while in others it may be a cloud-based memory device. In some embodiments, the digital camera is connected to a camera control unit which in various embodiments may be configured to overlay the output from with the one or more computer processors with the video stream.
In some embodiments, the system further comprises a display monitor. In some embodiments, the one or more computer processors comprise a central processing unit or a Graphical Processing Unit (also referred to a s GPU). In some embodiments, the system further comprises a mechanism to receive an input from the at least one operator (to activate or stop marking the landmark) intraoperatively. In various embodiments, the mechanism is configured to receive the input via one or more of a push-button, a touchscreen device, a pointing device, (e.g., a mouse or a head mounted pointing device), a foot pedal, a gesture recognition system, or a voice recognition system. In some embodiments, the one or more landmarks are tracked during the arthroscopic or other medical procedure. In some embodiments, the tracking of one or more landmarks is associated with the set of coordinates of the one or more landmarks relative to at least one or more of an anatomical structure, a injury or pathology or the structure, an implant placed in the structure or a repair of the structure.
In some embodiments, the displaying the one or more landmarks are overlaid on the displaying video stream. In some embodiments, the one or more landmarks are displayed as the relative anatomical structure is identified in the video stream. In some embodiments, the operator can select to render the one or more landmarks temporarily invisible or throughout the arthroscopic or other medical procedure.
Another aspect of the invention provides systems for assisting an arthroscopic or other medical procedure by allowing computer-implemented arbitrary landmark placement using radiological imaging, the system comprising one or more computer processors and one or more non-transitory computer-readable storage media storing instructions that are operable, when executed by the one or more computer processors, to cause the one or more computer processors to perform operations. In some embodiments, the operations comprise: receiving at least one radiological image of a subject; identifying one or more anatomical features in the at least one radiological image with a trained machine learning algorithm; generating a 3D representation of the identified anatomical features; receiving a location of one or more landmarks from an operator; overlaying the one or more landmarks on the 3D representation of the anatomical structures; and displaying the overlay on a displaying device to be used by the operator. Again Application of embodiments of the above system to the assistance of other medical procedures including minimally invasive procedures such as endoscopic, laparoscopic, and interventional cardiovascular procedures is also contemplated.
In some embodiments, the anatomical features comprise a bony structure or a tendon. In some embodiments, the at least one radiological image comprises one or more of an MRI scan, a CT scan, a PET scan, an ultrasound image or a combination thereof. In some embodiments, the at least one radiological image includes an image of a landmark. In some embodiments, the operations further comprise identifying the location of the landmark. In some embodiments, the operations further comprise recommending a location for a landmark based at least in part on the identified location of the landmark in at least one radiological image.
In some embodiments, the at least one radiological image or the one or more landmarks are blended with a video stream from an imaging device. In some embodiments, the blended image is displayed on a displaying device. In some embodiments, the displaying of the blended image occurs during the arthroscopic or other medical procedure. In some embodiments, the imaging device is an interventional imaging device such as an ultrasound imaging device or a fluoroscopic imaging device. In various embodiments, the video stream may be monocular or stereoscopic and the system can be configured recognize either to toggle back and for between either type and generate the associated output accordingly.
Another aspect of the current invention provides computer-implemented methods for assisting an arthroscopic or other medical procedure. In some embodiments, the methods comprise: receiving a video stream from an imaging device; receiving one or more sets of coordinates of one or more landmarks; overlaying the one or more landmarks on the video stream; and displaying the overlay on one or more display devices intraoperatively to be used by an operator during the arthroscopic or other medical procedure. Application of embodiments of the above methods to the assistance of other medical procedures including minimally invasive procedures such as endoscopic, laparoscopic, and interventional cardiovascular procedures is also contemplated.
In some embodiments, the method further comprises identifying and labeling one or more elements in the video stream using at least one of a trained computer algorithm, where the one or more elements comprise one or more of an anatomical structure, a surgical tool, an operational procedure or action, or a pathology. In some embodiments, identifying and labeling the one or more elements in the video stream comprises using one or more modules. In some embodiments, the one or more modules comprise one or more modules for video stream decomposition, tool recognition, anatomy recognition, tool tracking, gesture recognition, landmark point registration, or anatomy and landmark tracking. In some embodiments, the one or more landmarks are recommended based at least partially on the identified elements.
In some embodiments, the method further comprises: storing the one or more sets of coordinates of one or more landmarks; changing a view of the display to omit the overlaid landmark from being displayed; reverting the view to the previous display; identifying the one or more set of coordinates for the one or more landmarks; and re-overlaying the one or more landmarks. In some embodiments, the operator activates the changing and the reverting steps. In some embodiments, the changing a view step is activated automatically based on a change in an identified anatomical structure or pathology.
In some embodiments, the one or more sets of coordinates of the one or more landmarks is provided by an operator intraoperatively. In some embodiments, the one or more sets of coordinates of the one or more landmarks is provided by an operator preoperatively. In some embodiments, the one or more sets of coordinates of the one or more landmarks is generated from one or more medical images of a subject. In some embodiments, the one or more medical images are radiological images. In some embodiments, the radiological images are generated using fluoroscopy, MRI, or CT scanning. In some embodiments, the video stream is provided by an arthroscope during an arthroscopic procedure. In some embodiments, the arthroscopic procedure is used in a rotator cuff implant surgery. In some embodiments, the arthroscopic procedure is used in an ACL tunnel placement in a knee surgery. In some embodiments, the video stream is monocular. In some embodiments, the video stream is stereoscopic.
In some embodiments, the receiving the one or more video stream from the digital camera is performed using a wired media connection. In some embodiments, a latency between receiving the input from the digital camera and displaying an overlay of the output and the videos stream is at most 40 milliseconds (ms) to accommodate a digital camera with about 24 frames per second (fps). In some embodiments, the latency between receiving the input from the digital camera and overlay the output and the videos stream is no more than a time between two consecutive frames from the digital camera. where the receiving the one or more video stream from the digital camera is performed using a network connection.
In some embodiments, the method is performed using one or more computer processing units. In some embodiments, the one or more computer processing units comprise a central processing unit or a Graphical Processing Unit. In some embodiments, the interventional imaging device is a digital camera. In some embodiments, the digital camera is mounted on a scope.
In some embodiments, the camera control unit is configured to control a light source and capture digital information produced by the digital camera. In some embodiments, the camera control unit is configured to convert the digital information produced by the digital camera into the video stream.
In some embodiments, the camera control unit records the digital information produced by the digital camera in a memory device which may be a local memory device resident or operatively coupled to a computer system that performs one on more operations/steps of the method or remote memory device such as a cloud-based memory device. In some embodiments, the digital camera is connected to a camera control unit. In some embodiments, the video stream is received from the camera control unit by the one or more computer processing units to be processed. In some embodiments, the camera control unit is configured to overlay the output from the process by one or more computer processing units onto the video stream. In some embodiments, the method further comprises a display monitor.
In some embodiments, the method further comprises utilizing a mechanism to receive an input from the at least one operator to activate or stop marking the landmark intraoperatively. In one or more embodiments, the mechanism may be configured to receive the input via a push-button, a touchscreen device, a foot pedal, a gesture recognition method, or a voice recognition method.
In some embodiments, the one or more landmarks are tracked during the arthroscopic or other medical procedure (endoscopic, laparoscopic, cardioscopic procedure). In some embodiments, the tracking of one or more landmarks is associated with the set of coordinates of the one or more landmarks relative to at least one of an anatomical structure, an injury or pathology of the structure, an implant in the structure or a repair of the structure. In some embodiments, the display of the one or more landmarks is blended with the displaying the video stream. In some embodiments, the one or more landmarks are displayed as the relative anatomical structure is identified in the video streaming. In some embodiments, the operator can select to render the one or more landmarks invisible temporarily or throughout the arthroscopic procedure.
Another aspect of the present invention provides computer-implemented methods for assisting an arthroscopic or other medical procedure by arbitrary landmark placement using radiological imaging. In some embodiments, the methods comprise: receiving at least one radiological image of a subject; identifying one or more anatomical features in the at least one radiological image a trained machine learning algorithm; generating a 3D representation of the identified one or more anatomical features; receiving a location of one or more landmarks from an operator; overlaying the one or more landmarks on the 3D representation of one or more anatomical features; and displaying the overlay on a displaying device to be used by the operator. Application of embodiments of the methods to the assistance of other medical procedures including minimally invasive procedures such as endoscopic, laparoscopic, and interventional cardiovascular procedures is also contemplated.
In some embodiments, the anatomical features comprise a bony structure or a tendon. In some embodiments, the at least one radiological image comprises one or more of an MRI scan, a CT scan, or a combination thereof. In some embodiments, the at least one radiological image includes an image of a landmark. In some embodiments, the method further comprises identifying the location of the landmark.
In some embodiments, the method further comprises recommending a location for a landmark based at least in part on the identified location of the landmark in at least one radiological image. In some embodiments, the at least one radiological image or the one or more landmarks are overlaid on the video stream from an imaging device. In some embodiments, the blended image is displayed on a displaying device. In some embodiments, the displaying of the blended image is during an arthroscopic procedure. In some embodiments, the imaging device is an interventional imaging device. In some embodiments, the video stream is monocular. In some embodiments, the video stream is stereoscopic.
Another aspect of the present invention provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.
Another aspect of the present invention provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.
Additional aspects and advantages of the present invention will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present invention are shown and described. As will be realized, the present invention is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.
The novel features of the present invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:
While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.
Various embodiments of the invention provide computer-implemented medical systems, devices, and methods for assisting surgeons in an intraoperative setting using AI. The systems, devices, and methods disclosed herein may improve upon existing methods of surgical landmark placement by providing a fast and reliable classification (e.g., real-time) of various elements involved in a surgical operation (e.g., surgical tools, anatomical features, operation procedures) and placement of a virtual landmark with high precision and accuracy based on the classification of various elements. For example, systems, devices, and methods provided herein may use AI methods (e.g., machine learning, deep learning) to build a classifier which improves a real-time classification of elements involved in a surgical operation and identifies a location of a landmark by intraoperative command from an operator (e.g., using a surgical probe) or by processing preoperative medical images (e.g., on MRI, CT scan, or fluoroscopy), where the preoperative medical images contains a landmark. An AI approach may leverage large datasets in order gain new insights from the datasets. The classifier model may improve real-time characterization of various elements involved in an operation which may lead to higher operation success rate. The classifier model may provide an operator (e.g., surgeon, operating room nurse, surgical technician) with information for more accurate placement of a virtual landmark which eliminates the shortcomings of a physical landmark. The virtual landmark is trackable, removable, or changeable by using a button. The virtual landmark may not inhibit physical movement of the surgical tools during the surgery. The systems and methods here can overlay a landmark on a video stream of the surgery on demand (e.g., to show or not display based on a command from an operator).
Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention and the described embodiments. However, the embodiments of the present invention are optionally practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. In the drawings, like reference numbers designate like or similar steps or components.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the claims. As used in the description of the embodiments and the appended claims, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
As used herein, the term “if” is optionally construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” is optionally construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.
As used herein, and unless otherwise specified, the term “about” or “approximately” means an acceptable error for a particular value as determined by one of ordinary skill in the art, which depends in part on how the value is measured or determined. In certain embodiments, the term “about” or “approximately” means within 1, 2, 3, or 4 standard deviations. In certain embodiments, the term “about” or “approximately” means within 30%, 25%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, or 0.05% of a given value or range.
As used herein, the terms “comprises,” “comprising,” or any other variation thereof, are intended to cover a nonexclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
As used herein, the terms “subject” and “patient” are used interchangeably. As used herein, the terms “subject” and “subjects” refers to a human being. In certain embodiments, the subject is going through a surgical operation. In certain embodiments, the subject is 0 to 6 months old, 6 to 12 months old, 1 to 5 years old, 5 to 10 years old, 10 to 15 years old, 15 to 20 years old, 20 to 25 years old, 25 to 30 years old, 30 to 35 years old, 35 to 40 years old, 40 to 45 years old, 45 to 50 years old, 50 to 55 years old, 55 to 60 years old, 60 to 65 years old, 65 to 70 years old, 70 to 75 years old, 75 to 80 years old, 80 to 85 years old, 85 to 90 years old, 90 to 95 years old or 95 to 100.
Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.
Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.
The term “surgical AI” or “surgical AI module”, as used herein, generally refer to a system, device, or method that uses Artificial Intelligence algorithms to assist before, during, and/or after a surgical operation. A surgical AI module can be defined as a combination of input data, machine learning or deep learning algorithms, training datasets, or other datasets.
The term “machine learning”, as used herein, may generally refer to computer algorithms that can improve automatically over time. Any description herein of machine learning can be applied to Artificial Intelligence, and vice versa, or any combination thereof.
As used herein, the terms “continuous,” “continuously” or any other variation thereof, generally refer to a substantially uninterrupted process or a process with time delay that is acceptable in the context of the process.
The terms “video stream”, or “video feed”, as used herein, refer to data generated by a digital camera. Video feed may be a sequence of static or moving pictures.
The terms “region,” “organ,” “tissue,” “structure”, as used herein, may generally refer to anatomical features of the human body. A region may be larger than an organ and may comprise an organ. An organ may comprise one or more tissue types and structures. A Tissue may refer to a group of cells structurally joined to complete a common function. A structure can refer to a part of a tissue. In some embodiments, a structure may refer to one or more parts of one or more tissues joined together to create an anatomical feature.
The terms “surgical field of view,” or “field of view,” as used herein, refer to the extent of visibility captured by an interventional imaging device. Field of view may refer to the extent of visual data captured by a digital camera that is observable by human eye.
The term “decision,” as described herein, may refer to outputs from a machine learning or AI algorithm. A decision may comprise labeling, classification, prediction, etc.
The term “interventional imaging device,” as used herein, generally refers to an imaging device used for medical purposes. The interventional imaging device may refer to an imaging device that is used in a surgical operation e.g. one or more of an arthroscope, cardioscope, endoscope or laparoscope other like device. The surgical operation, in some embodiments, may be a simulation of an operation or other medical procedure.
The term “operator,” used herein, refers to a medical professional involved in a surgical operation. An operator can be a surgeon, an operating room nurse, a surgical technician.
The term “landmark”, “arbitrary landmark”, “virtual landmark”, and “fiducial marker” are as used interchangeably herein to refer to marks used to guide surgical or other medical procedures.
One aspect of the invention provides a system for assisting an arthroscopic procedure by allowing computer-implemented arbitrary landmark placement. The system may comprise one or more computer processors and one or more non-transitory computer-readable storage media storing instructions that are operable, when executed by the one or more computer processors, to cause the one or more computer processors to perform operations comprising: receiving a video stream from an arthroscopic imaging device; receiving one or more sets of coordinates of one or more landmarks; overlaying the one or more landmarks on the video stream; and displaying the overlay on one or more display devices intraoperatively to be used by an operator during the arthroscopic procedure. In some embodiments, an operator (e.g., a surgeon) provides the one or more sets of coordinates of one or more landmarks preoperatively.
The video stream may be provided by the arthroscopic imaging device during the arthroscopic procedure. In some embodiments, the arthroscopic imaging device comprises a digital camera. The video stream may be obtained from a digital camera specialized for an arthroscopic procedure. The digital camera may be mounted on a rigid scope, suitable for work in the arthroscopic joints. The scope may comprise an optical fiber which illuminates a field of view of the surgery. The digital camera may be mounted to a camera control unit. In some embodiments, a camera control unit is configured to capture digital information produced by the digital camera. In some embodiments, the camera control unit converts the digital information produced by the digital camera into the video stream. In some embodiments, the camera control unit is configured to control a light source. In some embodiments, the camera control unit is configured to record the digital information produced by the digital camera in a memory device. In some embodiments, the memory device used to record the digital information is a local memory device. In some embodiments, the camera control unit is configured to overlay the output from the one or more computer processors with the video stream. In some embodiments, the memory device is a remote or cloud-based memory device. The camera control unit may send the video stream to the one or more computer processors. In some embodiments, there is more than one camera control units. In some embodiments, there are 2, 3, 4, 5, or more camera control units. The one or more camera control units may send the video streams to the computer processors via network connection or a wired media connection. The video stream may be stereoscopic or monocular. In some embodiments, the system further comprises a display monitor. In some embodiments, the system comprises a mechanism to receive an input from the at least one operator (e.g., to activate or stop marking the landmark) intraoperatively. In some embodiments, the mechanism receives the input via a push-button, a touchscreen device, a foot pedal, a gesture recognition system, or a voice recognition system.
In some embodiments, the one or more sets of coordinates are provided during the surgery using a digital pointer (e.g., a computer mouse or related device) that can mark an image in the video stream to select a point or a region for performing surgery and/or a surgical action (e.g., tissue resection, ablation, etc.). In some embodiments, an operator (e.g., a surgeon) provides the one or more sets of coordinates intraoperatively by indicating the desired location using a standard surgical probe. In some embodiments, after the coordinates of a desired location are selected or indicated, an operator can issue a command so the system can register the location. In some embodiments, the system receives the register command from the operator via a push-button, a touchscreen device, a foot pedal, a gesture recognition system, or a voice recognition system.
In some embodiments, the arthroscope may generate consecutive images (e.g., a video feed) at a rate of at least about 10 frames per second (fps). In some embodiments, there is a latency in the system, which is the time of required to receive an image (e.g., a video feed) and provide an overlay (e.g., a processed image). In some other cases, two consecutive frames from the video stream (e.g., video stream input 301) may be generated at a speed of 1/fps (frames per second). In some embodiments, the latency in the system is at most 1/fps. The latency of the system may be less than the inverse of the rate of consecutive image generation of the surgical camera. For example, when the input signal is streaming at 20 frames per second, the latency may be equal or less than 1/20 (1/fps) or 50 ms. In some embodiments, the latency may comprise a period of rest in the system.
In some embodiments, the operations may further comprise identifying and labeling one or more elements in the video stream using at least one of a trained computer algorithm, where the one or more elements comprise one or more of an anatomical structure, a surgical tool, an operational procedure or action, or a pathology. In some embodiments, identifying and labeling the one or more elements in the video stream comprises using one or more AI modules. In some embodiments, the one or more AI module may comprise one or more modules for video stream decomposition, tool recognition, anatomy recognition, tool tracking, gesture recognition, landmark point registration, or anatomy and landmark tracking. In some embodiments, the system recommends one or more landmarks based at least partially on the identified elements. In some embodiments, the system recommends one or more landmarks based at least partially on the identified elements.
In some embodiments, the arthroscopic procedure comprise removal, or resection of an inflamed tissue and/or frayed tendons. In some embodiments, the arthroscopic procedure is used in a removal of or a resection of one or more inflamed tissues. In some embodiments, the arthroscopic procedure is used in a removal of or a resection of one or more frayed tendons where the video stream is monocular. For example, radiological imaging or other imaging methods such as fluoroscopic imaging can be used to locate the place of a landmark. An image obtained from these imaging methods can be provided to the system to be overlaid on the video stream during the surgery. In some embodiments, the system shows a latent anatomical structure or pathology; for example, the operator (e.g., a surgeon) may protect the ureter as the system visualizes the location of the ureter although it may not be exposed during a surgical procedure. For example, the system can ingest information from an external system such as a fluoroscopic imaging system. The landmark may then take the form of the vascularity rendered visible by the fluorescent dye imaged using the fluoroscopic imaging system. The system may continue to retain and track vascularity during the procedure (e.g., arthroscopic surgery).
In some embodiments, the system 100 operates on stored video content. A video recording of an arthroscopic surgery can be played back and sent to an interface. The system may then overlay any landmark on the video stream as explained herein. In some embodiments, the landmark placement on a recording of a surgery is used for training purposes. In some embodiments, the system operates on stereoscopic video streams. In some embodiments, the system can be used during a robotic arthroscopic procedure (e.g., a surgery). In some embodiments, a view of the display may be changed. In some embodiments, by changing a view a landmark that is overlaid on a video stream can be omitted from being displayed. In some embodiments, the operator can select to render the landmark invisible temporarily or throughout the arthroscopic procedure. The operator may revert the view to a previous display. The operator may identify new sets of coordinates for a landmark. The new landmark may be overlaid on to the video stream to be displayed. A plurality of landmarks may be selected to be displayed simultaneously or one at a time. In some embodiments, a change in the view is automatic. In some embodiments, changing a view may be cause by the AI pipeline identifying an anatomical structure or pathology.
In some embodiments, a set of coordinates of the landmark is provided by an operator intraoperatively.
In some embodiments, the tool recognition module 304 uses an AI network to recognize surgical tools in the field of view. Non-limiting examples of the AI network used in tool recognition module 304 may comprise Mask R-CNN, UNET, ResNET, YOLO, YOLO-2, or any combination thereof. In some embodiments, the AI networks are trained to recognize surgical tools of interest using machine learning training comprising architecture-specific training techniques. In some embodiments, the trained AI network detects the presence of a surgical tool in an image and outputs a mask. The mask may be a set of pixels extracted from the input image, which indicate the precise outline of the surgical tool. In some embodiments, the AI network outputs a box (e.g., a rectangular region) in which the tool is detected or displayed.
In some embodiments, the anatomy recognition module 305 uses an AI network to recognize an anatomical structure in a field of view. Non-limiting examples of the AI network used in anatomy recognition module 305 may comprise Mask R-CNN, UNET, ResNET, YOLO, YOLO-2, or any combination thereof. In some embodiments, the AI networks are trained to recognize anatomical structures of interest using architecture-specific training techniques. In some embodiments, the trained AI network recognizes anatomical structures as they are sighted in the field of view. In some embodiments, the trained network outputs pixel masks, which may indicate the precise outline of the recognized anatomical structure. In some embodiments, the trained network outputs a box (e.g., a rectangular region) in which the tool was detected or is displayed.
In some embodiments, an output from tool recognition module 304 is provided to a tool tracking module 306. In some embodiments, the tool tracking module 306 tracks the motion of the one or more tools identified by the tool recognition module 304. In some embodiments, a position of a tool (e.g., an instantaneous position of the tool) may be stored in a memory (e.g., a buffer). In some embodiments, tool tracking module 306 uses CV algorithms to compute the velocity and acceleration of the tool and stores these values in the memory. This data may be stored as a fixed length array. In some embodiments, this array is stored in the time order that they were captured. In some embodiments, the array is stored in descending order of time. The array may have a fixed length and with adding new data, an older entry may be dropped out of the array and the memory buffer. In some embodiments, adding a new entry causes the oldest entry to be dropped out of the array. An output of the tool tracking module 306 may comprise the mask of the recognized tool along with the array of the tool's velocity and/or acceleration. The tool tracking module 306 may supply a position or an array of the positions of one or more tool to a gesture recognition module 307 and a landmark registration module 308 (e.g., a bluDot point registration module).
In some embodiments, the gesture recognition module 307 uses an AI network comprising a memory (e.g., a recurrent neural network (RNN)), to interpret the movement of the tools. In some embodiments, the AI network is trained to recognize specific tools and/or identify specific movement patterns. For example, a tap would involve the tool moving in a specific manner relative to the background anatomy. In some embodiments, the surgeon can indicate a position of an arbitrary landmark by using a predetermined gesture using a surgical tool. Non-limiting example of a gesture may comprise tapping, double tapping, triple tapping, wagging (e.g., moving a tool from left to right). In some embodiments, gesture recognition module 307 outputs a label of the gesture made by an operator using a tool. In some embodiments, gesture recognition module 307 recognizes a gesture made by the operator and generates a label of the name of the recognized gesture to be supplied to a downstream component, which may be landmark registration module 308.
In some embodiments, the landmark registration module 308 receives one or more inputs from tool tracking module 306 and/or gesture recognition module 307, as described herein. In some embodiments, the input from gesture recognition module 307 instructs landmark registration module 308 that a gesture from an operator is recognized. The gesture may then be mapped to an action. In some embodiments, the mapping is configured preoperatively and is loaded from a database when the system is initialized. Non-limiting examples of an action mapped by 3 landmark registration module 08 may comprise to initiate, to replace, to clear all. In some embodiments, landmark registration module 308 may be initiated to assign a unique identifier to a landmark (e.g., a bluDot). An action comprising a command to clear one or more landmarks or clear all may activate landmark registration module 308 to update a list of one or more landmarks. An initiate action may trigger landmark registration module 308 to supply the location of the tool to anatomy and landmark tracking component 309. A replace action may trigger landmark registration module 308 to replace the data associated with the location of one or more landmarks with a location of a new landmark. A Clear all action may trigger landmark registration module 308 to clear any landmark that is being displayed or is stored in the memory. In some embodiments, landmark registration module 308 receives a direct input from the operator to place, replace, or clear a landmark. The direct input may be provided using a digital or mechanical button, for example, a foot-pedal-press or the press of a dedicated button on the arthroscopic device. In some embodiments, the CCU communicates the direct input through a custom interface to the VAIP, described herein. For example, a custom interface may comprise a gesture mapped to an action that is customized for an operator.
In some embodiments, landmark registration module 308 makes a distinction between the manner in which the landmarks are specified (e.g., preoperatively, intraoperatively via a gesture, intraoperatively via direct command from an operator) for rendering and/or recall purposes. For example, a landmark obtained from preoperative planning would be saved from deletion. In some embodiments, landmark registration module 308 supplies a set of coordinates of a tool identified by tool recognition module 304 in the image of the surgical field of view. The updated list in landmark registration module 308 may be passed down to a downstream anatomy and landmark tracking component 309, which may stop tracking and displaying landmarks that may be set for being cleared. For example, fluorescent dyes may be injected in the blood vessels to assist an operator (e.g., a surgeon) in identifying an artery or a vein. In some embodiments, the surgery is performed in close proximity to highly vascular regions, where nicking an artery or a vein can have serious consequences. An image of the identified arteries or veins (e.g., by using a dye) may be received and overlaid on the surgical video by VAIP as a landmark. A confidence may be calculated, substantially continuously, representing a certainty in the accuracy of VAIP to track and recall the landmark (e.g., identified arteries or veins). In some embodiments, the confidence may lower than a threshold, where the threshold may be about 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% or lower. The threshold may be set by the operator. In some embodiments, the change in confidence is due to changes in the surgical field of view (e.g., operation being performed may change the anatomical structure). The system may then indicate that the confidence in tracking the landmark has diminished. The operator may feature the landmark by, for example, injecting dyes into the blood vessels. The system may then replace the previously identified landmark with newly identified landmark and overlay the landmark on the surgical image (e.g., video stream).
In some embodiments, the anatomy and landmark tracking component 309 receives an anatomy mask from anatomy recognition module 305 and/or an identified tool mask from tool recognition module 304 in a substantially continuous manner. In some embodiments, anatomy and landmark tracking component 309 receives an input from landmark registration module 308 that indicates an action, anatomy and landmark tracking component 309 performs a series of operations. The series of operations may comprise determining the superposition of the tool and the anatomical structure from the mask to determine over which anatomical structure the tool is placed or held. The coordinates of the tool and the anatomical structure may be used to identify the overlap of the tool with the anatomical structure. The series of operations may further comprise extracting a feature to locate a landmark (e.g., a bluDot) in relation to a location of one or more anatomical structures. In some embodiments, the feature comprises a detail on the image, which may comprise a pattern of vascularity, an edge of tissue, or a locally unique patch of pixels. Using the feature, the system may stabilize the landmark (e.g., bluDot) against a movement of the camera with respect to an anatomical structure (also shown in
In some embodiments, the landmark is initialized and designated at an initial position of the tool. In some embodiments, anatomy and landmark tracking component 309 identifies changes in a location of a feature on an anatomical structure and re-designate the location of the landmark. In some embodiments, anatomical structures are modeled as mildly deformable solids and suitable computational techniques are used to track the features on the anatomical structures. In some embodiments, anatomy and landmark tracking component 309 acquires a feature continuously and tracks a movement of the landmark on an expanded canvas. An expanded canvas may comprise the surgical field of views in different images acquired from the video stream that may be connected to one another to generate a larger field of view. In some embodiments, using the feature described herein, the system tracks the landmark with a high degree of certainty even if the landmark or the underlying anatomical structure move off the camera's field of view. In some embodiments, during surgery, the operator might move the camera away from the location of the landmark and surrounding tissues causing the operator to lose sight of the landmarks. In some embodiments, the location of the landmark needs to be re-acquire when the operator returns to the general area again. In some case, an anatomical structure is recognized first, as described before, excluding any tools in the field of view. One or more features may be identified to recognize the location of the landmark according to an anatomical structure that has been recognized before.
For example, one or more feature points may be identified that may be separated by the anatomical structures on which they appear. When the surgeon reenters a surgical field of view that has been analyzed in a previous image, the anatomical recognition module(s) may recognize the previously processed image. Upon matching the new coordinates of the feature points in the current image with the coordinates of the feature points in the previously processed image, the landmark may be placed in its location. The location of the landmark may be reestablished based on the feature points in the current image as well as the previously identified feature points. The feature points matching process may be repeated to increase the accuracy of landmark placement. This process may be performed using parallel computing (e.g., a GPU). The system may discard the feature points identified in the previously processed image and replace them with the feature points identified in the current image. The processed described herein may be performed using the anatomy and landmark tracking module 309, out of range recognition module 310, and anatomy reacquisition module 311.
In some embodiments, the output from the landmark location module 320 is overlaid onto the video stream input from module 301 in a video blend module 312. The output from video blend module 312 may be displayed on output video stream 330 (e.g., with a screen, a monitor, a TV, a laptop screen, etc.) The output from 320 may be directed to the camera control unit to be scaled and overlaid onto the video stream of the procedure.
Another aspect of the invention provides a system for assisting an arthroscopic procedure by allowing computer-implemented arbitrary landmark placement using radiological imaging. The system may comprise one or more computer processors and one or more non-transitory computer-readable storage media storing instructions that are operable, when executed by the one or more computer processors, to cause the one or more computer processors to perform operations comprising: receiving a radiological image of a subject; generating a 3D representation of the radiological image; identifying an anatomical structure in the radiological image with a trained machine learning algorithm; receiving a location of a landmark from an operator; overlaying the landmark on the 3D representation of the radiological image; and displaying the overlay on a displaying device to be used by the operator.
In some embodiments, an operator identifies or set a location of a landmark during a preoperative surgery planning phase. In some embodiments, the landmark may be set by an operator (e.g., a surgeon) on a radiology images obtained from a subject. The landmark may then be supplied to a landmark registration module similar to landmark registration module 308 in
In some embodiments, the system comprises a recommender module that can recommend placement of a landmark based at least in part on the surgical context.
In some embodiments, based at least on the identified context 501 recommends the placement of a landmark. Non-limiting examples of recommendations from 501 may comprise a femoral and tibial tunnel placement in an anterior cruciate ligament (ACL) surgery, or an anchor placement in a Rotator Cuff Repair surgery. In some embodiments, 501 recommends the location of a landmark based at least in part on the location of the landmark in a preoperative medical image of the subject identified by 3D image reconstruction module 403 and/or mapping module 404. The recommended landmark and/or landmark location may be sent to landmark registration module 308 to be processed, overlaid on the video stream of the surgery and to be displayed on a displaying device (e.g., a monitor), as described herein before. In some embodiments, a preoperatively acquired image (e.g., MRI, CT scan, etc.) may be processed as described herein combined with tool tracking module 306 to provide the information required to estimate a size or location of a landmark. For example, imaging modalities (e.g., CT Scan, MRI) may produce images containing anatomical features that can be recognized by the system as described herein. The images may further comprise a location of a landmark. In some embodiments, these images are three dimensional images comprising voxels, where a voxel, (volumetric pixel), can represent a volume in physical space. Therefore, a location of a landmark may be identified on a surgical field of view image by matching the identified anatomical structures (e.g., by recognizing anatomical features on a preoperative image and the surgical image). The location of the landmark may be further identified based in part by measuring a size of the anatomical structure based in part on the size of the voxels in the preoperative image. This measurement may be used to place the landmark on a location on the anatomical structure on the surgical image corresponding to the location of the landmark identified on the preoperative image (e.g., CT scan, MRI).
In some embodiments, the system is configured to process a video stream from a stereoscopic surgical camera (e.g., a stereoscopic arthroscope).
In some embodiments, an object (e.g., a probe or surgical tool) 801 may be placed at the same location of a landmark 802. The system may identify the tool 801 as described herein and compensate for any occlusions (
Various embodiments of the invention also provide computer systems that are programmed to implement methods of the invention. Accordingly, a description of one or more embodiments of such computer systems will now be described.
The computer system 701 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 705, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 701 also includes memory or memory location 710 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 715 (e.g., hard disk), communication interface 720 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 725, such as cache, other memory, data storage and/or electronic display adapters. The memory 710, storage unit 715, interface 720 and peripheral devices 725 are in communication with the CPU 705 through a communication bus (solid lines), such as a motherboard. The storage unit 715 can be a data storage unit (or data repository) for storing data. The computer system 701 can be operatively coupled to a computer network (“network”) 730 with the aid of the communication interface 720. The network 730 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 730 in some embodiments is a telecommunication and/or data network. The network 730 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 730, in some embodiments with the aid of the computer system 701, can implement a peer-to-peer network, which may enable devices coupled to the computer system 701 to behave as a client or a server.
The CPU 705 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 710. The instructions can be directed to the CPU 705, which can subsequently program or otherwise configure the CPU 705 to implement methods of the present invention. Examples of operations performed by the CPU 705 can include fetch, decode, execute, and writeback.
The CPU 705 can be part of a circuit, such as an integrated circuit. One or more other components of the system 701 can be included in the circuit. In some embodiments, the circuit is an application specific integrated circuit (ASIC).
The storage unit 715 can store files, such as drivers, libraries and saved programs. The storage unit 715 can store user data, e.g., user preferences and user programs. The computer system 701 in some embodiments can include one or more additional data storage units that are external to the computer system 701, such as located on a remote server that is in communication with the computer system 701 through an intranet or the Internet.
The computer system 701 can communicate with one or more remote computer systems through the network 730. For instance, the computer system 701 can communicate with a remote computer system of a user (e.g., a portable computer, a tablet, a smart display device, a smart tv, etc.). Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 701 via the network 730.
Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 701, such as, for example, on the memory 710 or electronic storage unit 715. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 705. In some embodiments, the code can be retrieved from the storage unit 715 and stored on the memory 710 for ready access by the processor 705. In some situations, the electronic storage unit 715 can be precluded, and machine-executable instructions are stored on memory 710.
The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.
Aspects of the systems and methods provided herein, such as the computer system 701, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium. In various embodiments machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks (including wireless and wired networks). Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.
Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.
The computer system 701 can include or be in communication with an electronic display 735 that comprises a user interface (UI) 740 for providing, for example, an overlay of the identified features on a video feed from an arthroscope or to provide a recommendation to an operator in the course of a surgery. Examples of UI's include, without limitation, a graphical user interface (GUI) and web-based user interface.
In various embodiments, the methods and systems of the present invention can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 705. The algorithm can, for example, receiving an image from an interventional imaging device, identifying a feature in the image using an image recognition algorithm, overlaying the features on a video feed on a display device, make recommendations or suggestion to an operator based on the identified feature in the image.
While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. Accordingly, it should be understood that the invention covers various alternatives, modifications, variations or equivalents to the embodiments of the invention described herein.
Also, elements, characteristics, or acts from one embodiment can be readily recombined or substituted with one or more elements, characteristics or acts from other embodiments to form numerous additional embodiments within the scope of the invention. Moreover, elements that are shown or described as being combined with other elements, can, in various embodiments, exist as standalone elements. Further, embodiments of the invention specifically contemplate the exclusion of an element, act, or characteristic, etc. when that element, act or characteristic is positively recited. Hence, the scope of the present invention is not limited to the specifics of the described embodiments but is instead limited solely by the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202041015993 | Apr 2020 | IN | national |
This PCT application claims priority to Indian Provisional Patent Application No. 202041015993, filed Apr. 13, 2020, and U.S. Provisional Application Nos. 63/030,721, filed May 27, 2020, and 63/143,380, filed Jan. 29, 2021, the contents of all of which are fully incorporated herein by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/027000 | 4/13/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63030721 | May 2020 | US | |
| 63143380 | Jan 2021 | US |
