WO2024189115A1

WO2024189115A1 - Markov transition matrices for identifying deviation points for surgical procedures

Info

Publication number: WO2024189115A1
Application number: PCT/EP2024/056736
Authority: WO
Inventors: Pinja ME HAIKKA; Carole RJ ADDIS
Original assignee: Digital Surgery Ltd
Current assignee: Digital Surgery Ltd
Priority date: 2023-03-15
Filing date: 2024-03-13
Publication date: 2024-09-19
Anticipated expiration: 2025-09-15

Abstract

Examples described herein provide a computer-implemented method that uses transition matrices for identifying deviation points for surgical procedures. A transition matrix can be generated based at least in part on workflows by counting a number of each classification of phase transitions. The transition matrix can be normalized to determine probabilities for each of the phase transitions and an action can be implemented based at least in part on the probabilities for each of the phase transitions. Aspects can also include computer program products and systems that use transition matrices for identifying deviation points for surgical procedures.

Description

MARKOV TRANSITION MATRICES FOR IDENTIFYING DEVIATION POINTS

FOR SURGICAL PROCEDURES

BACKGROUND

[0001] Computer-assisted systems, particularly computer-assisted surgery systems (CASs), rely on video data digitally captured during a surgery. Such video data can be stored and/or streamed. In some cases, the video data can be used to augment a person’s physical sensing, perception, and reaction capabilities. For example, such systems can effectively provide the information corresponding to an expanded field of vision, both temporal and spatial, that enables a person to adjust current and future actions based on the part of an environment not included in his or her physical field of view. Alternatively, or in addition, the video data can be stored and/or transmitted for several purposes such as archival, training, post-surgery analysis, and/or patient consultation. The process of analyzing and comparing a large amount of video data from multiple surgical procedures to identify commonalities can be highly subjective and error-prone due, for example, to the volume of data and the numerous factors (e.g., patient condition, physician preferences, and/or the like including combinations and/or multiples thereof) that impact the workflow of each individual surgical procedure that is being analyzed.

SUMMARY

[0002] In one aspect, a computer-implemented method is provided that includes segmenting each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure and classifying the phase transitions. Workflows are generated for each of the plurality of videos based at least in part on the phase transitions. A transition matrix is generated based at least in part on the workflows by counting a number of each classification of phase transitions. The transition matrix is normalized to determine probabilities for each of the phase transitions. An action is implemented based at least in part on the probabilities for each of the phase transitions. [0003] In another aspect, a computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing device to cause the processing device to perform operations that include segmenting, using a trained machine learning model, each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure. The operations also include generating workflows for each of the plurality of videos based at least in part on the phase transitions and generating a transition matrix based at least in part on the workflows by counting a number of each classification of phase transitions. The operations further include normalizing the transition matrix to determine probabilities for each of the phase transitions and implementing an action based at least in part on the probabilities for each of the phase transitions.

[0004] In a further aspect, a system includes a memory including computer readable instructions and a processing device for executing the computer readable instructions. The computer readable instructions control the processing device to perform operations including generating a transition matrix based at least in part on workflows generated for each of a plurality of videos of a type of a surgical procedure by counting a number of each classification of phase transitions, normalizing the transition matrix to determine probabilities for each of the phase transitions, and implementing an action based at least in part on the probabilities for each of the phase transitions.

[0005] The above features and advantages, and other features and advantages, of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the aspects of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0007] FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects described herein;

[0008] FIG. 2 depicts a surgical procedure system according to one or more aspects described herein;

[0009] FIG. 3 depicts a system for analyzing video captured by a video recording system according to one or more aspects described herein;

[0010] FIG. 4 depicts a flow diagram of a method according to one or more aspects described herein;

[0011] FIG. 5A depicts a flow diagram of performing phase segmentation to generate a workflow from a video according to one or more aspects described herein;

[0012] FIGS. 5B, 5C, and 5D depict phase transitions according to one or more aspects described herein;

[0013] FIG. 6A depicts workflows for a plurality of videos of a surgical procedure according to one or more aspects described herein;

[0014] FIG. 6B depicts a transition matrix according to one or more aspects described herein;

[0015] FIG. 6C depicts a normalized transition matrix according to one or more aspects described herein;

[0016] FIG. 7 depicts a second order transition matrix according to one or more aspects described herein; [0017] FIG. 8 depicts a flow diagram of a method for identifying deviation points for surgical procedures using transition matrices according to one or more aspects described herein; and

[0018] FIG. 9 depicts a block diagram of a computer system according to one or more aspects described herein.

[0019] The diagrams depicted herein are illustrative. There can be many variations to the diagrams and/or the operations described herein without departing from the spirit of the invention. For instance, the actions can be performed in a differing order, or actions can be added, deleted, or modified. Also, the term “coupled” and variations thereof describe having a communications path between two elements and do not imply a direct connection between the elements with no intervening elements/connections between them. All of these variations are considered a part of the specification.

DETAILED DESCRIPTION

[0020] Aspects described herein relate in general to computing technology and relates more particularly to Markov transition matrices for identifying deviation points for surgical procedures.

[0021] Turning now to FIG. 1, an example computer-assisted system (CAS) system 100 is generally shown in accordance with one or more aspects. The CAS system 100 includes at least a computing system 102, a video recording system 104, and a surgical instrumentation system 106. As illustrated in FIG. 1, an actor 112 can be medical personnel that uses the CAS system 100 to perform a surgical procedure on a patient 110. Medical personnel can be a surgeon, assistant, nurse, administrator, or any other actor that interacts with the CAS system 100 in a surgical environment. The surgical procedure can be any type of surgery, such as but not limited to cataract surgery, laparoscopic cholecystectomy, endoscopic endonasal transsphenoidal approach (eTSA) to resection of pituitary adenomas, or any other surgical procedure. In other examples, actor 112 can be a technician, an administrator, an engineer, or any other such personnel that interacts with the CAS system 100. For example, actor 112 can record data from the CAS system 100, configure/update one or more attributes of the CAS system 100, review past performance of the CAS system 100, repair the CAS system 100, and/or the like including combinations and/or multiples thereof.

[0022] A surgical procedure can include multiple phases, and each phase can include one or more surgical actions. A “surgical action” can include an incision, a compression, a stapling, a clipping, a suturing, a cauterization, a sealing, or any other such actions performed to complete a phase in the surgical procedure. A “phase” represents a surgical event that is composed of a series of steps (e.g., closure). A “step” refers to the completion of a named surgical objective (e.g., hemostasis). During each step, certain surgical instruments 108 (e.g., forceps) are used to achieve a specific objective by performing one or more surgical actions. In addition, a particular anatomical structure of the patient may be the target of the surgical action(s).

[0023] The video recording system 104 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, and/or the like including combinations and/or multiples thereof. The cameras 105 capture video data of the surgical procedure being performed. The video recording system 104 includes one or more video capture devices that can include cameras 105 placed in the surgical room to capture events surrounding (i.e., outside) the patient being operated upon. The video recording system 104 further includes cameras 105 that are passed inside (e.g., endoscopic cameras) the patient 110 to capture endoscopic data. The endoscopic data provides video and images of the surgical procedure.

[0024] The computing system 102 includes one or more memory devices, one or more processors, a user interface device, among other components. All or a portion of the computing system 102 shown in FIG. 1 can be implemented for example, by all or a portion of computer system 900 of FIG. 9. Computing system 102 can execute one or more computer-executable instructions. The execution of the instructions facilitates the computing system 102 to perform one or more methods, including those described herein. The computing system 102 can communicate with other computing systems via a wired and/or a wireless network. In one or more examples, the computing system 102 includes one or more trained machine learning models that can detect and/or predict features of/from the surgical procedure that is being performed or has been performed earlier. Features can include structures such as anatomical structures, surgical instruments 108 in the captured video of the surgical procedure. Features can further include events such as phases and/or actions in the surgical procedure. Features that are detected can further include the actor 112 and/or patient 110. Based on the detection, the computing system 102, in one or more examples, can provide recommendations for subsequent actions to be taken by the actor 112. Alternatively, or in addition, the computing system 102 can provide one or more reports based on the detections. The detections by the machine learning models can be performed in an autonomous or semi-autonomous manner.

[0025] The machine learning models can include artificial neural networks, such as deep neural networks, convolutional neural networks, recurrent neural networks, encoders, decoders, or any other type of machine learning model. The machine learning models can be trained in a supervised, unsupervised, or hybrid manner. The machine learning models can be trained to perform detection and/or prediction using one or more types of data acquired by the CAS system 100. For example, the machine learning models can use the video data captured via the video recording system 104. Alternatively, or in addition, the machine learning models use the surgical instrumentation data from the surgical instrumentation system 106. In yet other examples, the machine learning models use a combination of video data and surgical instrumentation data.

[0026] Additionally, in some examples, the machine learning models can also use audio data captured during the surgical procedure. The audio data can include sounds emitted by the surgical instrumentation system 106 while activating one or more surgical instruments 108. Alternatively, or in addition, the audio data can include voice commands, snippets, or dialog from one or more actors 112. The audio data can further include sounds made by the surgical instruments 108 during their use. [0027] In one or more examples, the machine learning models can detect surgical actions, surgical phases, anatomical structures, surgical instruments, and various other features from the data associated with a surgical procedure. The detection can be performed in real-time in some examples. Alternatively, or in addition, the computing system 102 analyzes the surgical data, i.e., the various types of data captured during the surgical procedure, in an offline manner (e.g., post-surgery). In one or more examples, the machine learning models detect surgical phases based on detecting some of the features such as the anatomical structure, surgical instruments, and/or the like including combinations and/or multiples thereof.

[0028] A data collection system 150 can be employed to store the surgical data, including the video(s) captured during the surgical procedures. The data collection system 150 includes one or more storage devices 152. The data collection system 150 can be a local storage system, a cloud-based storage system, or a combination thereof. Further, the data collection system 150 can use any type of cloud-based storage architecture, for example, public cloud, private cloud, hybrid cloud, and/or the like including combinations and/or multiples thereof. In some examples, the data collection system can use a distributed storage, i.e., the storage devices 152 are located at different geographic locations. The storage devices 152 can include any type of electronic data storage media used for recording machine-readable data, such as semiconductor-based, magnetic-based, optical-based storage media, and/or the like including combinations and/or multiples thereof. For example, the data storage media can include flash-based solid-state drives (SSDs), magnetic-based hard disk drives, magnetic tape, optical discs, and/or the like including combinations and/or multiples thereof.

[0029] In one or more examples, the data collection system 150 can be part of the video recording system 104, or vice-versa. In some examples, the data collection system 150, the video recording system 104, and the computing system 102, can communicate with each other via a communication network, which can be wired, wireless, or a combination thereof. The communication between the systems can include the transfer of data (e.g., video data, instrumentation data, and/or the like including combinations and/or multiples thereof), data manipulation commands (e.g., browse, copy, paste, move, delete, create, compress, and/or the like including combinations and/or multiples thereof), data manipulation results, and/or the like including combinations and/or multiples thereof. In one or more examples, the computing system 102 can manipulate the data already stored/being stored in the data collection system 150 based on outputs from the one or more machine learning models (e.g., phase detection, anatomical structure detection, surgical tool detection, and/or the like including combinations and/or multiples thereof). Alternatively, or in addition, the computing system 102 can manipulate the data already stored/being stored in the data collection system 150 based on information from the surgical instrumentation system 106.

[0030] In one or more examples, the video captured by the video recording system 104 is stored on the data collection system 150. In some examples, the computing system 102 curates parts of the video data being stored on the data collection system 150. In some examples, the computing system 102 filters the video captured by the video recording system 104 before it is stored on the data collection system 150. Alternatively, or in addition, the computing system 102 filters the video captured by the video recording system 104 after it is stored on the data collection system 150.

[0031] Turning now to FIG. 2, a surgical procedure system 200 is generally shown according to one or more aspects described herein. The example of FIG. 2 depicts a surgical procedure support system 202 that can include or may be coupled to the CAS system 100 of FIG. 1. The surgical procedure support system 202 can acquire image or video data using one or more cameras 204. The surgical procedure support system 202 can also interface with one or more sensors 206 and/or one or more effectors 208. The sensors 206 may be associated with surgical support equipment and/or patient monitoring. The effectors 208 can be robotic components or other equipment controllable through the surgical procedure support system 202. The surgical procedure support system 202 can also interact with one or more user interfaces 210, such as various input and/or output devices. The surgical procedure support system 202 can store, access, and/or update surgical data 214 associated with a training dataset and/or live data as a surgical procedure is being performed on patient 110 of FIG. 1. The surgical procedure support system 202 can store, access, and/or update surgical objectives 216 to assist in training and guidance for one or more surgical procedures. User configurations 218 can track and store user preferences.

[0032] Turning now to FIG. 3, a system 300 for analyzing video and data is generally shown according to one or more aspects described herein. In accordance with aspects, the video and data is captured from video recording system 104 of FIG. 1. The analysis can result in predicting features that include surgical phases and structures (e.g., instruments, anatomical structures, and/or the like including combinations and/or multiples thereof) in the video data using machine learning. System 300 can be the computing system 102 of FIG. 1, or a part thereof in one or more examples. System 300 uses data streams in the surgical data to identify procedural states according to some aspects.

[0033] System 300 includes a data reception system 305 that collects surgical data, including the video data and surgical instrumentation data. The data reception system 305 can include one or more devices (e.g., one or more user devices and/or servers) located within and/or associated with a surgical operating room and/or control center. The data reception system 305 can receive surgical data in real-time, i.e., as the surgical procedure is being performed. Alternatively, or in addition, the data reception system 305 can receive or access surgical data in an offline manner, for example, by accessing data that is stored in the data collection system 150 of FIG. 1.

[0034] System 300 further includes a machine learning processing system 310 that processes the surgical data using one or more machine learning models to identify one or more features, such as surgical phase, instrument, anatomical structure, and/or the like including combinations and/or multiples thereof, in the surgical data. It will be appreciated that machine learning processing system 310 can include one or more devices (e.g., one or more servers), each of which can be configured to include part or all of one or more of the depicted components of the machine learning processing system 310. In some instances, a part or all of the machine learning processing system 310 is cloudbased and/or remote from an operating room and/or physical location corresponding to a part or all of data reception system 305. It will be appreciated that several components of the machine learning processing system 310 are depicted and described herein. However, the components are just one example structure of the machine learning processing system 310, and that in other examples, the machine learning processing system 310 can be structured using a different combination of the components. Such variations in the combination of the components are encompassed by the technical solutions described herein.

[0035] The machine learning processing system 310 includes a machine learning training system 325, which can be a separate device (e.g., server) that stores its output as one or more trained machine learning models 330. The trained machine learning models 330 are accessible by a machine learning execution system 340. The machine learning execution system 340 can be separate from the machine learning training system 325 in some examples. In other words, in some aspects, devices that “train” the models are separate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained machine learning models 330.

[0036] Machine learning processing system 310, in some examples, further includes a data generator 315 to generate simulated surgical data, such as a set of virtual images, or record the video data from the video recording system 104, to generate trained machine learning models 330. Data generator 315 can access (read/write) a data store 320 to record data, including multiple images and/or multiple videos. The images and/or videos can include images and/or videos collected during one or more procedures (e.g., one or more surgical procedures). For example, the images and/or video may have been collected by a user device worn by the actor 112 of FIG. 1 (e.g., surgeon, surgical nurse, anesthesiologist, and/or the like including combinations and/or multiples thereof) during the surgery, a non-wearable imaging device located within an operating room, an endoscopic camera inserted inside the patient 110 of FIG. 1, and/or the like including combinations and/or multiples thereof. The data store 320 is separate from the data collection system 150 of FIG. 1 in some examples. In other examples, the data store 320 is part of the data collection system 150.

[0037] Each of the images and/or videos recorded in the data store 320 for performing training (e.g., generating the trained machine learning models 330) can be defined as a base image and can be associated with other data that characterizes an associated procedure and/or rendering specifications. For example, the other data can identify a type of procedure, a location of a procedure, one or more people involved in performing the procedure, surgical objectives, and/or an outcome of the procedure. Alternatively, or in addition, the other data can indicate a stage of the procedure with which the image or video corresponds, rendering specification with which the image or video corresponds and/or a type of imaging device that captured the image or video (e.g., and/or, if the device is a wearable device, a role of a particular person wearing the device, and/or the like including combinations and/or multiples thereof). Further, the other data can include image-segmentation data that identifies and/or characterizes one or more objects (e.g., tools, anatomical objects, and/or the like including combinations and/or multiples thereof) that are depicted in the image or video. The characterization can indicate the position, orientation, or pose of the object in the image. For example, the characterization can indicate a set of pixels that correspond to the object and/or a state of the object resulting from a past or current user handling. Localization can be performed using a variety of techniques for identifying objects in one or more coordinate systems.

[0038] The machine learning training system 325 uses the recorded data in the data store 320, which can include the simulated surgical data (e.g., set of virtual images) and/or actual surgical data to generate the trained machine learning models 330. The trained machine learning models 330 can be defined based on a type of model and a set of hyperparameters (e.g., defined based on input from a client device). The trained machine learning models 330 can be configured based on a set of parameters that can be dynamically defined based on (e.g., continuous or repeated) training (i.e., learning, parameter tuning). Machine learning training system 325 can use one or more optimization algorithms to define the set of parameters to minimize or maximize one or more loss functions. The set of (learned) parameters can be stored as part of the trained machine learning models 330 using a specific data structure for a particular trained machine learning model of the trained machine learning models 330. The data structure can also include one or more non-learnable variables (e.g., hyperparameters and/or model definitions).

[0039] Machine learning execution system 340 can access the data structure(s) of the trained machine learning models 330 and accordingly configure the trained machine learning models 330 for inference (e.g., prediction, classification, and/or the like including combinations and/or multiples thereof). The trained machine learning models 330 can include, for example, a fully convolutional network adaptation, an adversarial network model, an encoder, a decoder, or other types of machine learning models. The type of the trained machine learning models 330 can be indicated in the corresponding data structures. The trained machine learning models 330 can be configured in accordance with one or more hyperparameters and the set of learned parameters.

[0040] The trained machine learning models 330, during execution, receive, as input, surgical data to be processed and subsequently generate one or more inferences according to the training. For example, the video data captured by the video recording system 104 of FIG. 1 can include data streams (e.g., an array of intensity, depth, and/or RGB values) for a single image or for each of a set of frames (e.g., including multiple images or an image with sequencing data) representing a temporal window of fixed or variable length in a video. The video data that is captured by the video recording system 104 can be received by the data reception system 305, which can include one or more devices located within an operating room where the surgical procedure is being performed. Alternatively, the data reception system 305 can include devices that are located remotely, to which the captured video data is streamed live during the performance of the surgical procedure. Alternatively, or in addition, the data reception system 305 accesses the data in an offline manner from the data collection system 150 or from any other data source (e.g., local or remote storage device).

[0041] The data reception system 305 can process the video and/or data received. The processing can include decoding when a video stream is received in an encoded format such that data for a sequence of images can be extracted and processed. The data reception system 305 can also process other types of data included in the input surgical data. For example, the surgical data can include additional data streams, such as audio data, RFID data, textual data, measurements from one or more surgical instrum ents/sensors, and/or the like including combinations and/or multiples thereof, that can represent stimuli/procedural states from the operating room. The data reception system 305 synchronizes the different inputs from the different devices/sensors before inputting them in the machine learning processing system 310.

[0042] The trained machine learning models 330, once trained, can analyze the input surgical data, and in one or more aspects, predict and/or characterize features (e.g., structures) included in the video data included with the surgical data. The video data can include sequential images and/or encoded video data (e.g., using digital video file/stream formats and/or codecs, such as MP4, MOV, AVI, WEBM, AVCHD, OGG, and/or the like including combinations and/or multiples thereof). The prediction and/or characterization of the features can include segmenting the video data or predicting the localization of the structures with a probabilistic heatmap. In some instances, the one or more trained machine learning models 330 include or are associated with a preprocessing or augmentation (e.g., intensity normalization, resizing, cropping, and/or the like including combinations and/or multiples thereof) that is performed prior to segmenting the video data. An output of the one or more trained machine learning models 330 can include image-segmentation or probabilistic heatmap data that indicates which (if any) of a defined set of structures are predicted within the video data, a location and/or position and/or pose of the structure(s) within the video data, and/or state of the structure(s). The location can be a set of coordinates in an image/frame in the video data. For example, the coordinates can provide a bounding box. The coordinates can provide boundaries that surround the structure(s) being predicted. The trained machine learning models 330, in one or more examples, are trained to perform higher-level predictions and tracking, such as predicting a phase of a surgical procedure and tracking one or more surgical instruments used in the surgical procedure.

[0043] While some techniques for predicting a surgical phase (“phase”) in the surgical procedure are described herein, it should be understood that any other technique for phase prediction can be used without affecting the aspects of the technical solutions described herein. In some examples, the machine learning processing system 310 includes a phase detector 350 that uses the trained machine learning models 330 to identify a phase within the surgical procedure (“procedure”). Phase detector 350 uses a particular procedural tracking data structure 355 from a list of procedural tracking data structures. Phase detector 350 selects the procedural tracking data structure 355 based on the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure is predetermined or input by actor 112. The procedural tracking data structure 355 identifies a set of potential phases that can correspond to a part of the specific type of procedure as “phase predictions.”

[0044] In some examples, the procedural tracking data structure 355 can be a graph that includes a set of nodes and a set of edges, with each node corresponding to a potential phase. The edges can provide directional connections between nodes that indicate (via the direction) an expected order during which the phases will be encountered throughout an iteration of the procedure. The procedural tracking data structure 355 may include one or more branching nodes that feed to multiple next nodes and/or can include one or more points of divergence and/or convergence between the nodes. In some instances, a phase indicates a procedural action (e.g., surgical action) that is being performed or has been performed and/or indicates a combination of actions that have been performed. In some instances, a phase relates to a biological state of a patient undergoing a surgical procedure. For example, the biological state can indicate a complication (e.g., blood clots, clogged arteries/veins, and/or the like including combinations and/or multiples thereof), pre-condition (e.g., lesions, polyps, and/or the like including combinations and/or multiples thereof). In some examples, the trained machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, and/or the like including combinations and/or multiples thereof.

[0045] Each node within the procedural tracking data structure 355 can identify one or more characteristics of the phase corresponding to that node. The characteristics can include visual characteristics. In some instances, the node identifies one or more tools that are typically in use or available for use (e.g., on a tool tray) during the phase. The node also identifies one or more roles of people who are typically performing a surgical task, a typical type of movement (e.g., of a hand or tool), and/or the like including combinations and/or multiples thereof. Thus, phase detector 350 can use the segmented data generated by machine learning execution system 340 that indicates the presence and/or characteristics of particular objects within a field of view to identify an estimated node to which the real image data corresponds. Identification of the node (i.e., phase) can further be based upon previously detected phases for a given procedural iteration and/or other detected input (e.g., verbal audio data that includes person-to-person requests or comments, explicit identifications of a current or past phase, information requests, and/or the like including combinations and/or multiples thereof).

[0046] The phase detector 350 outputs the phase prediction associated with a portion of the video data that is analyzed by the machine learning processing system 310. The phase prediction is associated with the portion of the video data by identifying a start time and an end time of the portion of the video that is analyzed by the machine learning execution system 340. The phase prediction that is output can include segments of the video where each segment corresponds to and includes an identity of a surgical phase as detected by the phase detector 350 based on the output of the machine learning execution system 340. Further, the phase prediction, in one or more examples, can include additional data dimensions such as, but not limited to, identities of the structures (e.g., instrument, anatomy, and/or the like including combinations and/or multiples thereof) that are identified by the machine learning execution system 340 in the portion of the video that is analyzed. The phase prediction can also include a confidence score of the prediction. Other examples can include various other types of information in the phase prediction that is output.

[0047] It should be noted that although some of the drawings depict endoscopic videos being analyzed, the technical solutions described herein can be applied to analyze video and image data captured by cameras that are not endoscopic (i.e., cameras external to the patient’s body) when performing open surgeries (i.e., not laparoscopic surgeries). For example, the video and image data can be captured by cameras that are mounted on one or more personnel in the operating room (e.g., surgeon). Alternatively, or in addition, the cameras can be mounted on surgical instruments, walls, or other locations in the operating room.

[0048] One or more aspects described herein provide for identifying points in a surgical workflow called deviation points. Deviation points are points during a surgical procedure where an agent (e.g., a surgeon) faces a decision about how to proceed. For example, a surgeon performing a gastric bypass procedure may decide to measure the biliopancreatic (BP) limb at the beginning of the procedure or later in the procedure, as shown in the example of FIG. 4.

[0049] This simple example illustrates two different paths for the surgical procedure stemming from the deviation point: one path 410 where the BP limb is measured at the beginning of the procedure and another path 420 where the BP limb is measured later in the procedure. In this example, it can be observed that measuring the BP limb at the beginning of the procedure (path 410) (e.g., immediately after port insertion), which occurred 47% of the time, adds approximately 5 minutes to the surgical procedure duration average. This contrasts with transitioning to omental division after port insertion (path 420), which occurred 53% of the time in this example.

[0050] A single surgical procedure can include many such deviation points. The decision to follow one path over another may affect measurable outcomes, such as the overall procedure duration, patient outcomes, and/or the like, including combinations and/or multiples thereof. Accordingly, it is useful to detect deviation points in surgical procedures.

[0051] Deviation points can be detected, for example, by identifying phases of a surgical procedure and analyzing the phase transitions using Markov transition matrices. Markov transition matrices (also known as Markov matrices, transition matrices, stochastic matrices, and probability matrices, among other names) are square matrices that describe probabilities of transitioning from one state to another. In the context of surgical procedures, transitions occur when one phase of a surgical procedure ends and the next phase of the surgical procedure begins. According to one or more aspects described herein, Markov transition matrices can be used to track and analyze these transitions.

[0052] For example, as shown in FIG. 5A, a video 501 of a surgical procedure can be analyzed (e.g., using the techniques described with respect to FIGS. 1-3) by performing phase segmentation 502 to generate phase segments. The phase segments indicate phases of the surgical procedure as described herein. In the example of FIG. 5 A, four phases are shown: phase A, phase B, phase C, and phase D, but the one or more aspects described herein are not so limited. The phase segments can be used to determine a workflow 503 of the phases. In this example, the workflow is as follows: phase A phase B

phase phase

phase D.

[0053] It can then be determined how often a transition from one phase to another phase in a surgical procedure is observed, and transitions can be classified based on their frequency. Accordingly, phase transitions can be classified as being atypical (e.g., FIG. 5B), common (e.g., FIG. 5C), or deviation points (e.g., FIG. 5D). For example, in FIG. 5A, a phase transition from phase A to phase B is determined to have occurred in 95% of cases of a particular type (e.g., a particular type of surgical procedure, such as a gastric bypass) while a phase transition from phase A to phase C is determined to have occurred in 5% of cases of the particular type. Thus, the phase transition from phase A to phase C can be classified as being atypical. Conversely, as shown in the example of FIG. 5B, a phase transition from phase A to phase B is considered common based on the frequency (e.g., 95%) of occurrence. Atypical and common transitions can be classified according to one or more thresholds. For example, transitions that occur less than an atypical threshold (e.g., 5%, 10%, 15%, and/or the like) can be considered atypical transitions, and transitions that occur more than a common threshold (e.g., 65%, 70%, 80%, 90%, and/or the like) can be considered common transitions. The atypical threshold and the common threshold can be adjusted or otherwise modified. Transitions that are not classified as being atypical transitions or common transitions are classified as deviation points, such as shown in FIG. 5D. In this example, the transition from phase A to phase B is determined to have occurred in 55% of cases and the transition from phase A to phase D is determined to have occurred in 45% of cases.

[0054] Once deviation points are identified, the deviation points can be analyzed to determine consequences of the decisions made at the deviation points and to determine what action(s) to take going forward. For example, insights can be provided to a surgeon linking a deviation point to overall case duration. The deviation points can also be analyzed to determine predictors of a deviation point and/or to identify insights into atypical transitions. Further, deviation points provide a tool for annotation quality assurance. For example, deviation points can be used to update the trained machine learning model that performs the segmentation, thus improving the trained machine learning model. This may lead to making corrections, such as to correct an incorrect phase transition identification. The deviation points may also be analyzed and compared to patient outcome data where available to determine how deviation points contributed to the patient outcome. [0055] FIG. 6A depicts workflows 600 for a plurality of videos of a surgical procedure of a certain type (e.g., a gastric bypass procedure) according to one or more aspects described herein. In this example, the workflows 600 include individual workflows 601, 602, 603, 604, 605, which are instances of the type of surgical procedure being performed. Although the type of surgical procedure is the same, the workflows differ due to different actions taken by the surgeon. Each of the workflows 600 includes multiple phases (and corresponding phase transitions), among phase A, phase B, phase C, and phase D, as shown.

[0056] As shown in FIG. 6B, workflows 600 are converted into a transition matrix 610 by counting the number of phase transitions occurring within the workflows 600 according to one or more aspects described herein. The transition matrix 610 is a representation of the phase transitions from the workflows 600 of FIG. 6 A, where the vertical axis represents a start phase, and the horizontal axis represents a next phase.

Each cell of the transition matrix 610 indicates a number of times a transition occurred based on the workflows 600. For example, a transition as follows: from phase A to phase B eight times, from phase A to phase D two times, from phase B to phase A three times, from phase B to phase C five times, from phase B to phase D four times, from phase C to phase B three times, from phase C to phase D one time, from phase D to phase A four times, from phase D to phase B one time, and from phase D to phase C one time. It should be appreciated that same phase transitions are not considered and are represented by an “X” in the transition matrix 610.

[0057] As shown in FIG. 6C, the transition matrix 610 is converted into a normalized transition matrix 620 according to one or more aspects described herein. The transition matrix 610 can be normalized per row to determine a probability of a phase transition for each state. For example, as shown in FIG. 6C, there is an 80% (0.8) probability to transition from phase A to phase B and a 20% (0.2) probability to transition from phase A to phase D. Other probabilities are calculated and shown in FIG. 6C. [0058] In some cases, it may be desirable to explore more complex phase transitions, such as two or more phase transitions (e.g., phase A to phase B to phase C). In such cases, a higher order transition matrix, such as a second order transition matrix, can be used. A second order transition matrix considers tuples of phases, which means that the transition probability is determined by the current phase as well as the previous phase. Thus, a “memory” is included in the model such that historical phase information is considered. FIG. 7 depicts an example of using a second order transition matrix 710 according to one or more aspects described herein. However, it should be appreciated that higher order transition matrices can also be used in other examples.

[0059] In particular, the second order transition matrix 710 is generated using the workflows 600 of FIG. 6A but is not so limited. According to one or more aspects described herein, padding is added to the start sequence to account for the first transition, as shown in the firs four rows of the second order transition matrix 710. In the example of FIG. 7, phase transitions are determined based on three phases. For example, one phase transition may be phase A to phase B to phase C, which occurred five times based on the workflows 600. Similarly, another phase transition may be phase A to phase B to phase D, which occurred three times based on the workflows 600. As another example, a phase transition may be phase B to phase C to phase B, which occurred two times based on the workflows 600. According to one or more aspects described herein, the second order transition matrix 710 can be converted into a normalized transition matrix (not shown) similar to the normalized transition matrix 620 of FIG. 6C. That is, the second order transition matrix 710 is normalized per row to determine a probability of a phase transition for each state.

[0060] Different diagnostic tests can be used to determine an appropriate order using the higher order transition matrix. Examples of such diagnostic tests include a likelihood ratio statistics, aikeke information criterion (AIC), Bayes information criterion (BIC), or other approaches. [0061] For likelihood ratio statistics, for each possible order, calculate the log likelihood and test if the higher order is improved by calculating the likelihood ratio statistic, which follows a chi-squared distribution. If p-value is below a threshold, reject the null (low order) in favor of alternative (higher order). Higher order Markov chains have exponentially more parameters than lower order models, which can cause potential overfitting, and therefore this approach may provide a better fit for the data and may be favored by their improvements in likelihoods. Also, a risk of a significant result if many tests are run, which can be mitigated using a Bonferroni correction.

[0062] AIC uses the likelihood ratio statistic but adds a penalty term that depends on the degrees of freedom in the model. In this example, a maximum order “m” is chosen as a reasonably high and test lower order models until an optimal order is found.

[0063] Similar to AIC, the BIC penalizes for the sample size in the model. In case of inequality, it may be possible to investigate the patterns further by simulating observations and investigate distinct sample sizes.

[0064] Another approach is a Bayesian method. This approach can avoid overfitting by introducing a penalty for increased complexity. Another approach is a cross-validation method that is useful for checking the robustness of results.

[0065] FIG. 8 depicts a flow diagram of a method 800 for identifying deviation points for surgical procedures using transition matrices according to one or more aspects described herein. The method 800 can be implemented using any suitable device and/or system, such as the processing system 900 shown in FIG. 9 and described in more detail herein.

[0066] At block 802, the processing system 900 segments each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure. According to one or more aspects described herein, the machine learning processing system 310 uses a trained machine learning model (e.g., trained machine learning models 330) to perform the segmenting as described herein. [0067] At block 804, the processing system 900 classifies the phase transitions. For example, as described herein with respect to FIGS. 5B, 5C, and 5D, the classifying can include classifying each of the phase transitions as being one of an atypical phase transition, a common phase transition, or a deviation point.

[0068] At block 806, the processing system 900 generates workflows (e.g., the workflows 600) for each of the plurality of videos based at least in part on the phase transitions.

[0069] At block 808, the processing system 900 generates a Markov transition matrix (e.g., the transition matrix 610 of FIG. 6B, the second order transition matrix 710 of FIG. 7) based at least in part on the workflows by counting a number of each classification of phase transitions. The Markov transition matrix can be a first order transition matrix or a higher order transition matrix, such as a second order transition matrix.

[0070] At block 810, the processing system 900 normalizes the transition matrix to determine probabilities for each of the phase transitions. For example, the processing system 900 generates a normalized transition matrix (e.g., the normalized transition matrix 620 of FIG. 6C), which shows probabilities for each of the phase transitions.

[0071] At block 812, an action is implemented based at least in part on the probabilities for each of the phase transitions. An example of an action is a surgical action (e.g., a surgeon takes a particular action based on probability for each of the phase transitions). Another example of an action is to re-segment the phase transitions. This may be useful to improve the segmenting based on results/probabilities. Another example of an action is to update a trained machine learning model that was used to perform the segmenting.

[0072] Additional processes also may be included. According to one or more aspects described herein, the method 800 can include associating information (e.g., a bookmark, a tag, an annotation, and a note) with one or more of the phase transitions. For example, a user can add information, a system can automatically add information, and/or the like, including combinations and/or multiples thereof. It should be understood that the process depicted in FIG. 8 represents an illustration, and that other processes may be added or existing processes may be removed, modified, or rearranged without departing from the scope of the present disclosure.

[0073] It is understood that one or more aspects described herein is capable of being implemented in conjunction with any other type of computing environment now known or later developed. For example, FIG. 9 depicts a block diagram of a processing system 900 for implementing the techniques described herein. In examples, processing system 900 has one or more central processing units (“processors” or “processing resources” or “processing devices”) 921a, 921b, 921c, etc. (collectively or generically referred to as processor(s) 921 and/or as processing device(s)). In aspects of the present disclosure, each processor 921 can include a reduced instruction set computer (RISC) microprocessor. Processors 921 are coupled to system memory (e.g., random access memory (RAM) 924) and various other components via a system bus 933. Read only memory (ROM) 922 is coupled to system bus 933 and may include a basic input/output system (BIOS), which controls certain basic functions of processing system 900.

[0074] Further depicted are an input/output (I/O) adapter 927 and a network adapter 926 coupled to system bus 933. I/O adapter 927 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 923 and/or a storage device 925 or any other similar component. I/O adapter 927, hard disk 923, and storage device 925 are collectively referred to herein as mass storage 934. Operating system 940 for execution on processing system 900 may be stored in mass storage 934. The network adapter 926 interconnects system bus 933 with an outside network 936 enabling processing system 900 to communicate with other such systems.

[0075] A display 935 (e.g., a display monitor) is connected to system bus 933 by display adapter 932, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one aspect of the present disclosure, adapters 926, 927, and/or 932 may be connected to one or more I/O busses that are connected to system bus 933 via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system bus 933 via user interface adapter 928 and display adapter 932. A keyboard 929, mouse 930, and speaker 931 may be interconnected to system bus 933 via user interface adapter 928, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.

[0076] In some aspects of the present disclosure, processing system 900 includes a graphics processing unit 937. Graphics processing unit 937 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit 937 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

[0077] Thus, as configured herein, processing system 900 includes processing capability in the form of processors 921, storage capability including system memory (e.g., RAM 924), and mass storage 934, input means such as keyboard 929 and mouse 930, and output capability including speaker 931 and display 935. In some aspects of the present disclosure, a portion of system memory (e.g., RAM 924) and mass storage 934 collectively store the operating system 940 such as the AIX® operating system to coordinate the functions of the various components shown in processing system 900.

[0078] It is to be understood that the block diagram of FIG. 9 is not intended to indicate that the computer system 900 is to include all of the components shown in FIG.

9. Rather, the computer system 900 can include any appropriate fewer or additional components not illustrated in FIG. 9 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the aspects described herein with respect to computer system 900 may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application-specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various aspects.

[0079] In one aspect, a computer-implemented method is provided that includes segmenting each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure and classifying the phase transitions. Workflows are generated for each of the plurality of videos based at least in part on the phase transitions. A transition matrix is generated based at least in part on the workflows by counting a number of each classification of phase transitions. The transition matrix is normalized to determine probabilities for each of the phase transitions. An action is implemented based at least in part on the probabilities for each of the phase transitions.

[0080] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the action is a surgical action.

[0081] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the action is re-segment the phase transitions.

[0082] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the segmenting is performed using a trained machine learning model.

[0083] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the action is updating the trained machine learning model. [0084] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include associating information with one or more of the phase transitions.

[0085] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the information is selected from a group consisting of a bookmark, a tag, an annotation, and a note.

[0086] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the classifying is based at least in part on a threshold.

[0087] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the classifying classifies each of the phase transitions as being one of an atypical phase transition, a common phase transition, or a deviation point.

[0088] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the transition matrix is a second order transition matrix.

[0089] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the transition matrix is a higher order transition matrix.

[0090] In addition to one or more of the features described herein, or as an alternative, further aspects of the method may include where the transition matrix is a Markov transition matrix.

[0091] In another aspect, a computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing device to cause the processing device to perform operations that include segmenting, using a trained machine learning model, each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure. The operations also include generating workflows for each of the plurality of videos based at least in part on the phase transitions and generating a transition matrix based at least in part on the workflows by counting a number of each classification of phase transitions. The operations further include normalizing the transition matrix to determine probabilities for each of the phase transitions and implementing an action based at least in part on the probabilities for each of the phase transitions.

[0092] In addition to one or more of the features described herein, or as an alternative, further aspects of the computer program product may include where the transition matrix is a Markov transition matrix.

[0093] In addition to one or more of the features described herein, or as an alternative, further aspects of the computer program product may include where the transition matrix is a second order transition matrix.

[0094] In a further aspect, a system includes a memory including computer readable instructions and a processing device for executing the computer readable instructions. The computer readable instructions control the processing device to perform operations including generating a transition matrix based at least in part on workflows generated for each of a plurality of videos of a type of a surgical procedure by counting a number of each classification of phase transitions, normalizing the transition matrix to determine probabilities for each of the phase transitions, and implementing an action based at least in part on the probabilities for each of the phase transitions.

[0095] In addition to one or more of the features described herein, or as an alternative, further aspects of the system may include where the operations further comprise segmenting each of the plurality of videos of the type of the surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure. [0096] In addition to one or more of the features described herein, or as an alternative, further aspects of the system may include where the operations further comprise segmenting each of the plurality of videos of the type of the surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure.

[0097] In addition to one or more of the features described herein, or as an alternative, further aspects of the system may include where the segmenting is performed using a trained machine learning model.

[0098] In addition to one or more of the features described herein, or as an alternative, further aspects of the system may include where the operations further include classifying the phase transitions.

[0099] The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer-readable storage medium (or media) having computer- readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0100] The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0101] Computer-readable program instructions described herein can be downloaded to respective computing/processing devices from a computer-readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network, and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer- readable program instructions for storage in a computer-readable storage medium within the respective computing/processing device.

[0102] Computer-readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source-code or object code written in any combination of one or more programming languages, including an object-oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some aspects, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer-readable program instruction by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0103] Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to aspects of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

[0104] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

[0105] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

[0106] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0107] The descriptions of the various aspects of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the aspects disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described aspects. The terminology used herein was chosen to best explain the principles of the aspects, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the aspects described herein.

[0108] Various aspects of the invention are described herein with reference to the related drawings. Alternative aspects of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.

[0109] The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains,” or “containing,” or any other variation thereof are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.

[0110] Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. The terms “at least one” and “one or more” may be understood to include any integer number greater than or equal to one, i.e., one, two, three, four, etc. The terms “a plurality” may be understood to include any integer number greater than or equal to two, i.e., two, three, four, five, etc. The term “connection” may include both an indirect “connection” and a direct “connection.”

[OHl] The terms “about,” “substantially,” “approximately,” and variations thereof are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ± 8% or 5%, or 2% of a given value. [0112] For the sake of brevity, conventional techniques related to making and using aspects of the invention may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and/or process details.

[0113] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

[0114] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0115] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

Claims

What is claimed is:

1. A computer-implemented method comprising: segmenting each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure; classifying the phase transitions; generating workflows for each of the plurality of videos based at least in part on the phase transitions; generating a transition matrix based at least in part on the workflows by counting a number of each classification of phase transitions; normalizing the transition matrix to determine probabilities for each of the phase transitions; and implementing an action based at least in part on the probabilities for each of the phase transitions.

2. The computer-implemented method of claim 1, wherein the action is a surgical action.

3. The computer-implemented method of claim 1 or claim 2, wherein the action is re-segment the phase transitions.

4. The computer-implemented method of any preceding claim, wherein the segmenting is performed using a trained machine learning model.

5. The computer-implemented method of claim 4, wherein the action is updating the trained machine learning model.

6. The computer-implemented method of any preceding claim, further comprising associating information with one or more of the phase transitions.

7. The computer-implemented method of claim 6, wherein the information is selected from a group consisting of a bookmark, a tag, an annotation, and a note.

8. The computer-implemented method of any preceding claim, wherein the classifying is based at least in part on a threshold.

9. The computer-implemented method of any preceding claim, wherein the classifying classifies each of the phase transitions as being one of an atypical phase transition, a common phase transition, or a deviation point.

10. The computer-implemented method of any preceding claim, wherein the transition matrix is a second order transition matrix.

11. The computer-implemented method of any preceding claim, wherein the transition matrix is a higher order transition matrix.

12. The computer-implemented method of any preceding claim, wherein the transition matrix is a Markov transition matrix.

13. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising: segmenting, using a trained machine learning model, each of a plurality of videos of a type of a surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure; generating workflows for each of the plurality of videos based at least in part on the phase transitions; generating a transition matrix based at least in part on the workflows by counting a number of each classification of phase transitions; normalizing the transition matrix to determine probabilities for each of the phase transitions; and implementing an action based at least in part on the probabilities for each of the phase transitions.

14. The computer program product of claim 13, wherein the transition matrix is a Markov transition matrix.

15. The computer program product of claim 13 or claim 14, wherein the transition matrix is a second order transition matrix.

16. A system comprising: a memory comprising computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations comprising: generating a transition matrix based at least in part on workflows generated for each of a plurality of videos of a type of a surgical procedure by counting a number of each classification of phase transitions; normalizing the transition matrix to determine probabilities for each of the phase transitions; and implementing an action based at least in part on the probabilities for each of the phase transitions.

17. The system of claim 16, wherein the operations further comprise segmenting each of the plurality of videos of the type of the surgical procedure into surgical phases to identify phase transitions during the type of the surgical procedure.

18. The system of claim 17, wherein the segmenting is performed using a trained machine learning model.

19. The system of claim 17 or claim 18, wherein the operations further comprise classifying the phase transitions.

20. The system of claim 19, wherein the operations further comprise generating the workflows for each of the plurality of videos based at least in part on the phase transitions.