Processing of video-based features for statistical modelling of surgical timings

Abstract

Disclosed is a method with steps of feeding a model with image data representing a surgical operation, including a target phase of the surgical operation, performed over a surgical field defined on a body of a patient, the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data including activity data of surgical activities performed during the surgical operation, deriving result data from the feature data, indicative of one or more of an operation trend and an operation inefficiency of the surgeon; and utilizing the result data to optimize performance of the surgeon during the target phase.

Description

PROCESSING OF VIDEO-BASED FEATURESFOR STATISTICAL MODELLING OF SURGICAL TIMINGSBACKGROUND

[0001] The present disclosure relates in general to computing technology and relates more particularly to computing technology for processing of video-based features for statistical modelling of surgical timings.

[0002] 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.SUMMARY

[0003] According to an aspect, a computer-implemented method is provided. The method includes feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by thesurgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model; wherein deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

[0004] According to another aspect, a computing system is provided. The system includes a data store including video data associated with a surgical procedure; and a machine learning training system configured for: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

[0005] According to another aspect, a computer program product including a memory device having computer executable instructions stored thereon is provided. When executed by one or more processors, the computer program product causes the one or more processors of a computing system to perform a plurality of operations including: feeding a model on a computing system with image data collected from a video recording system, including a camera, representingvideo recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency the surgeon relative to other surgeons for which data was collected and utilized for training the model; and utilizing the result data, to optimize performance of the surgeon during the target phase.

[0006] 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

[0007] 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 disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0008] FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects;

[0009] FIG. 2 depicts a surgical procedure system according to one or more aspects;

[0010] FIG. 3 depicts a system for analyzing video and data according to one or more aspects;

[0011] FIG. 4 depicts a method of utilizing a model to identify trends and inefficiencies of a targeted phase of an operation according to one or more aspects;

[0012] FIG. 5 depicts additional details about a method of identifying by the computing system from the image data, a workflow performed during the operation by the surgeons according to one or more aspects;

[0013] FIG. 6 depicts aspects of the identified workflow according to one or more aspects;

[0014] FIG. 7 depicts additional details of collecting the metadata on surgeons that execute procedures according to one or more aspects;

[0015] FIG. 8 depicts a graphical feature matrix obtained from the collected surgical activity data according to one or more aspects;

[0016] FIG. 9 depicts a flowchart showing a method of building the model that identifies trends and inefficiencies of a targeted phase of an operation from the identified feature data according to one or more aspects;

[0017] FIG. 10 depicts a process map showing aspects of the method of FIG. 9 according to one or more aspects;

[0018] FIG. 11 depicts a flowchart showing a method of populating an insight matrix from the model according to one or more aspects;

[0019] FIG. 12 depicts an illustration of graphically providing insights to a surgeon based on the collected and processed data according to one or more aspects;

[0020] FIG. 13 shows another flowchart of identifying surgeon trends and inefficiencies utilizing the disclosed system and method; and

[0021] FIG. 14 depicts a block diagram of a computer system according to one or more aspects.

[0022] 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 described aspects. 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

[0023] Exemplary aspects of the technical solutions described herein include systems and methods for utilizing video-based features for statistical modelling of surgical timings.

[0024] The disclosed embodiments enable predicting the duration of a specific phase of an operation and enable an understanding the factors that influence that duration. The embodiments utilize video-derived metrics as dependent factors or variables in a model to determine influencing factors of the duration of the procedure phase. The influencing factors are identified to determine whether and why an average duration of an individual surgeon is different from a benchmark value. The embodiments are applied to identify surgical inefficiencies and optimize phase durations at a granular level. The embodiments can be utilized to generate insights that guide surgeons to optimize their performance.

[0025] Technical solutions are described herein to address such technical challenges. Particularly, technical solutions herein collect video data and information from operations performed by surgeons, and utilize a model to analyze the data to identify inefficiencies and provide insights for minimizing the inefficiencies.

[0026] 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.

[0027] 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).

[0028] 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 includecameras 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.

[0029] 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 1900 of FIG. 14. 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.

[0030] The machine learning models can include artificial neural networks, such as deep neural networks, convolutional neural networks, recurrent neural networks, vision transformers, 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 theCAS 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.

[0031] 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.

[0032] 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., postsurgery). 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.

[0033] 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 differentgeographic 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.

[0034] 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.

[0035] 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 102filters the video captured by the video recording system 104 after it is stored on the data collection system 150.

[0036] Turning now to FIG. 2, a surgical procedure system 200 is generally shown according to one or more aspects. 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.

[0037] Turning now to FIG. 3, a system 300 for analyzing video and data is generally shown according to one or more aspects. 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.

[0038] 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 caninclude 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.

[0039] 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 cloud-based 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.

[0040] 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 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 realtime processing of surgical data using the trained machine learning models 330.

[0041] Machine learning processing system 310, in some examples, further includes a data generator 315 to generate simulated surgical data, such as a set of synthetic images and / or synthetic video, in combination with real image and 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.

[0042] Each of the images and / or videos recorded in the data store 320 for performing training (e.g., generating the 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 poseof 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.

[0043] 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 synthetic images and / or synthetic video) 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-leamable variables (e.g., hyperparameters and / or model definitions).

[0044] 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.

[0045] 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).

[0046] 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 instruments / 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.

[0047] 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, suchas 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 imagesegmentation 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.

[0048] 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 detector 350 that uses the trained machine learning models 330 to identify various items or states within the surgical procedure (“procedure”). The detector 350 can use a particular procedural tracking data structure 355 from a list of procedural tracking data structures. The detector 350 can select 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 can be predetermined or input by actor 112. For instance, the procedural tracking data structure 355 can identify a set of potential phases that cancorrespond to a part of the specific type of procedure as “phase predictions”, where the detector350 is a phase detector.

[0049] 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.

[0050] 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, 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 furtherbe 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).

[0051] The detector 350 can output predictions, such as a 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 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. Further, other types of outputs of the detector 350 can include state information or other information used to generate audio output, visual output, and / or commands. For instance, the output can trigger an alert, an augmented visualization, identify a predicted current condition, identify a predicted future condition, command control of equipment, and / or result in other such data / commands being transmitted to a support system component, e.g., through surgical procedure support system 202 of FIG. 2.

[0052] ft 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 imagedata 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. Alternatively, or in addition, the video can be images captured by other imaging modalities, such as ultrasound.

[0053] As indicated, the disclosed embodiments enable predicting the duration of a specific phase of an operation and enable an understanding the factors that influence that duration. The embodiments utilize video-derived metrics as dependent factors or variables in a model to determine influencing factors of the duration of the procedure phase. The influencing factors are identified to determine why individual cases are different from a benchmark value. The embodiments are applied to identify surgical inefficiencies and optimize phase durations at a granular level. The embodiments can be utilized to generate insights that guide surgeons to optimize their performance.

[0054] FIG. 4 shows a method of utilizing a model, which may be implemented on the computing system 100 (e.g., FIG. 1), to identify trends and inefficiencies of a targeted phase of an operation. With further reference to FIG. 4, as shown in block 1010, the method includes feeding a model, which may be a multilevel, multivariate model (discussed in greater detail below) or other statistical model or machine learning model indicated herein, on the computing system 102 with image data (or physical movement data) from the video recording system 104 representing video recordings of a target phase of a selected operation performed by a plurality of surgeons, each of the each of the surgeons performing the entire operation and / or the targeted phase of the operation a plurality of times. Specifically, in one recorded and analyzed set of operations, the selected operation was a right hemicolectomy and the target phase is vascular pedicle dissection and ligation (medial to lateral mobilization). A hemicolectomy is a surgical procedure where a portion of the colon (large intestine) is removed. This procedure may be performed to treat conditions like colon cancer, inflammatory bowel disease (such as Crohn's disease or ulcerative colitis), diverticulitis, or severe trauma to the colon. Vascular pedicle dissection and ligation is a surgical technique usedto control blood flow to a specific area of the body by identifying and cutting (dissecting) the main blood vessels (pedicles) supplying that area, and then tying them off (ligation) to prevent bleed- ingl. This method is used in various types of surgeries, such as organ resections (e.g., kidney or liver surgery) and cancer treatments, to minimize blood loss and ensure a clear surgical field.

[0055] Clinically, the target phase may be considered one of the more crucial and timeconsuming phases in the operation. Surgical activity data was collected for one hundred and fifty- five (155) procedures performed among eleven (11) surgeons. Each surgeon performed between two (2) and thirty -nine (39) of the procedures.

[0056] As shown in block 1020A, the method includes collecting feature data (i.e., dependent variable data for the model) by the computing system 102. This includes identifying by the computing system 102 from the image data, surgical activity data (or activity data) of surgical activities performed during the operation by the surgeons. The activity data includes but is not limited to (1) camera out repetitions per procedure (block 1020i); (2) phase repetitions (block 1020ii); (3) surgical approach (block 1020iii); (4) whether there was colon mobilization before vascular pedicles (block 1020iv); and (5) whether the operating field was exposed during phase (block 1020v).

[0057] The camera may be moved out of the operating field to clean the lens or to provide additional viewing for the surgeon. A surgical approach may include, for example, with respect to a robotic prostatectomy, this procedure may be performed utilizing a posterior or an anterior approach. A phase may be repeated to confirm all aspects of a procedure were completed accurately. Colon mobilization before vascular pedicles refers to the process of moving and freeing up the colon from its surrounding structures before addressing the main blood vessels (vascular pedicles) that supply it. This step may be performed in surgeries like colectomies, where the surgeon dissects and mobilizes the colon to ensure clear access to the vascular pedicles for ligation (tying off) and to minimize blood loss during the procedure. Exposing the operating field during surgery meanscreating a clear and accessible area around the surgical site. This involves making an incision and using various implements, such as retractors, to hold tissues and organs aside, providing the surgeon with an unobstructed view and ability to reach the area being treated, while minimizing risk of injury to surrounding structures. It is to be appreciated that with greater skill, the time or occurrence of one or more of these steps may be minimized or avoided.

[0058] As shown in block 1020B collecting the feature data by the computing system 102 further includes collecting metadata related to the surgeons. The metadata includes but is not limited to (1) surgeon experience level (block 1020vi); (2) surgeon specialty (block 1020vii); and, in certain cases, (3) geographic location of the operation surgeon (block 1020viii). It is to be appreciated that surgeons may have experience based on expertise performing certain procedures as well as a total number of years in practice. It is to be further appreciated that surgical practices may differ geographically based on local norms.

[0059] As shown in block 1030 the method includes deriving, by the computing system 102, result data (i.e., independent variable data for the model) from the feature data. The result data is indicative one or more of operation trends and operation inefficiencies for each of the surgeons. As shown in block 1040, the method includes identifying and displaying on a display, e.g., the display device 410 (FIG. 12), by the computing system 102, the results data. From this, performance of the surgeons during the target phase is optimized, e.g., in later executions of the operation.

[0060] In the recorded and analyzed set of operations, the surgeon experience level had two options. Specifically, the surgeons were classified as attending or resident. The eleven surgeons included eight (8) attending and three (3) resident surgeons. The surgeon specialty had two options. Specifically, the surgeon specialties were colorectal and non-colorectal, e.g., other. The eleven (11) surgeons included ten (10) surgeons with specialties in colorectal and one (1) was non- colorectal. The camera out repetitions per operation recorded between (0) zero and (26) (twenty-six) repeats (one or more times) per procedure. Sixty eight percent (68%) of the procedures had no camera out repetition. In one embodiment, phase repetitions per procedure recorded one (1) to six (6) repetitions per procedure. Sixty seven percent (67%) of the procedures had no phase repetition. The surgical approach included twenty -five (25) intracorporeal cases from one (1) surgeon. There were twenty -two (22) cases from five (5) of the eleven (11) surgeons in which there was colon mobilization before vascular pedicles. There were eight (8) operations, from five of the eleven surgeons, without exposure to the operating field. Regarding the target phase duration, the median duration was twenty -three (23) minutes, with a range of five (5) to one hundred (100) minutes and an interquartile range (IQR) of sixteen (16) to thirty-five (35) minutes.

[0061] FIG. 5 shows additional details about a method of identifying (e.g., parsing) by the computing system 102 from the image data, a workflow performed during the operation by the surgeons. FIG. 6 illustrates aspects of the identified workflow.

[0062] As indicated in block 1010, above, and repeated in FIG. 5, the method includes uploading the plurality of video recordings 1212 of the operation performed by the plurality of surgeons to the model on the computing system 102. It is to be appreciated that the videos 1212 contain image data 1224 which contains the activity data 1225 component of the feature data. As shown in FIG. 6, three sets of video recordings 1212A, 1212B, 1212C are captured for three recordings of the operation, e.g., from one or more surgeons. As shown in block 1120, the method includes applying by the model, or otherwise by the computing system 102, a phase detection algorithm 1222 to the image data in the video recordings 1212 to obtain segmented phase data (i.e., segmented surgical phase data) for each of the video recordings 1212. That is, for each of the video recordings 1212A-1212C, the algorithm segments the data into distinct surgical phases. The phases may be identified as phases A, B, C and D, which are further addressed with the discussion of FIG. 8, below. One of the phases, such as phase D, for example, is the target phase, i.e., vascular pedicle dissection and ligation. The segmentation may be augmented by human annotators.

[0063] As shown in block 1130 the method includes extracting by the computing system 102 from the segmented phase data, phase workflow data generally referenced as 1232. Specifically, from the different operation video recordings 1212A-1212C, the phase workflow data is respectively referenced as 1232A, 1232B, 1232C, respectively indicating:

[0064] Workflow from recorded video 1212A: A, B, A, C, D

[0065] Workflow from recorded video 1212B: A, B, C, A, D; and

[0066] Workflow from recorded video 1212C: A, C, B, C, B, D

[0067] Sample phases A-C are discussed in greater detail with the discussion of FIG. 8, below. As indicated with FIG. 8, phases A-C could be, for example specimen extraction and extracorporeal anastomosis, preparation for anastomosis, incorporeal anastomosis, specimen retrieval and hemostasis and visual inspection.

[0068] The phase workflow data 1232 includes an order of sequence of and duration of the phases, and surgical activities occurring during the phases. This enables the system 102 to extract, e.g., the duration of a surgeon performing the target phase, e.g., the phase D of vascular pedicle dissection and ligation and surgical activities during that phase. This also enables the system 102 to identify whether and how often any of the phases, such as the target phase, and surgical activities within the target phase, such as camera extraction, are repeated.

[0069] As shown in block 1140, the method includes obtaining, by the computing system 102, camera extraction data generally referenced as 1242, from the phase workflow data 1222 for the target phase. This data may be extracted utilizing a RedactOR algorithm. This data represents a time and duration of withdrawing the laparoscopic camera from the patient during the target phase in each operation, such as to clean the camera lens. Specifically, the camera extraction data is referred to as 1242A1, 1242A2 for two withdrawals during the first operation video 1212A dur-ing the target phase. The camera extraction data is referred to as 1242B1-1242B4 for four withdrawals during the second operation video 1212B. The camera out duration identified by 1242B2, the second extraction, is longer than the other extractions during the second operation captured by the second video recording 1212B, as indicated by the relatively longer shaded line in the figure. The camera was not withdrawn during the third operation video 1212C.

[0070] FIG. 7 shows additional details of collecting the metadata on surgeons that execute operations. With further reference to FIGS. 5 and 7, as shown in block 1150 the method includes collecting the metadata component of the feature data, generally referenced as 1252, on the surgeon, such as their experience level (FIG. 4, block 1020vi), their specialty (FIG. 4, block 1020vii), and other information identified for the dependent variables, which may include a geographic location of the operation (FIG. 4, block 1020viii). This information typically may be collected any time prior to executing an analysis of the video recordings 1212 by the model on the computing system 102. It is to be appreciated that the combination of the activity data 1226 and the metadata 1252 results in the feature data 1228.

[0071] FIG. 8 shows a graphical feature matrix 1220 obtained from the collected activity data. The data represents workflow data 1232A-1232F from six of the operation video recordings 1212A-1232F.

[0072] As indicated with block 1130, above the method includes extracting, from the phase segmentations, phase workflow data 1232 from each video 1212 obtained during each respective operation. The phase workflow data 1232 may include the different phases of the operation such as (in no particular order): vascular pedicle dissection and ligation (medial to lateral mobilization) 1410 (i.e., the target phase), specimen extraction and extracorporeal anastomosis 1420, preparation for anastomosis 1430, incorporeal anastomosis 1440, specimen retrieval 1450 and hemostasis and visual inspection 1460. The designation of Phase D in FIG. 6 in one embodiment relates to vascular pedicle dissection and ligation (medial to lateral mobilization) 1410, i.e., the target phase. It is tobe appreciated that the designation of Phases A-C in FIG. 6 may relate to any of specimen extraction and extracorporeal anastomosis 1420, preparation for anastomosis 1430, incorporeal anastomosis 1440, specimen retrieval 1450 and hemostasis and visual inspection 1460.

[0073] Anastomosis is a surgical connection between two structures, typically tubular ones like blood vessels or segments of the intestine. This technique may be applied in procedures where part of an organ is removed or damaged and the remaining sections need to be joined together to restore normal function. Extracorporeal anastomosis is a surgical technique where the connection (anastomosis) of two sections of the bowel is performed outside the body. This may involve removing the bowel segment, bringing it outside through an incision, and then connecting the two ends before reinserting the bowel back into the body. This method may be used in laparoscopic surgeries for conditions like colon cancer. During intracorporeal anastomosis, the connection is made entirely within the body without exteriorizing the bowel. Hemostasis is the process by which the body stops bleeding.

[0074] The phase workflow data 1232 may also show the activity data recorded while the surgeon performs the operation during the target phase, including colon mobilization (FIG. 4, block 1020iv) and operating field exposure (FIG. 4, block 1020v).

[0075] With the extracted activity data, a table of the operation feature matrix may be populated, as indicated below (TABLE 1), where each row represents activity data recorded from one operation. The columns represent, for each operation: (1) whether and how many times for an operation the target phase of vascular pedicles is performed and repeated (1410 of FIG. 8), (2) whether the approach was intracorporeal or extracorporeal (block 1020iii of FIG. 4), (3) whether colon mobilization performed before vascular pedicles (block 1020iv of FIG. 4), and (4) whether the operating field was exposed (block 1020v of FIG. 4).

[0076] As indicated in the data, for the first three listed operations, the target phase is repeated twice while in the next two operations it is performed once. In each operation, the approachis intracorporeal. In the first four listed operations, there is no colon mobilization before vascular pedicles while in the fifth listed operation there was colon mobilization. In each listed operation, there was an operating field exposure. It can be appreciated that the table would be populated for all collected operational data for each operation by each surgeon.TABLE 1

[0077] FIG. 9 is a flowchart showing a method of building the model, e.g., which populated the feature matrix (TABLE 1). As indicated, model identifies trends and inefficiencies of a targeted phase of an operation from the identified feature data. FIG. 10 shows a process map showing aspects of the method of FIG. 9.

[0078] As shown in block 1505, the method includes populating, by the computing system 102, a feature data matrix 1610 (TABLE 1) from the feature data. As shown in block 1510, the method includes ingesting, by the computing system 102, the feature data matrix 1610 into statistical software 1620. For example, R from The R Foundation may be utilized, as well as other software (e.g., SPSS from IBM or Stata from StataCorp LLC). As shown in block 1520, themethod includes fitting the feature data into a multilevel, multivariate model.

[0079] A multilevel, multivariate model is a type of statistical model that can analyze multiple outcome variables that vary at more than one level of a hierarchical or nested data structure. A multilevel, multivariate model can account for the dependencies and variations among the outcome variables and the levels of data, as well as the interactions and correlations among them. More specifically, multilevel modeling deals with data that has a hierarchical or nested structure, i.e., a matrix of information where one or more elements of the matrix contains a further matrix of information. Multivariate analysis involves examining multiple dependent variables simultaneously to understand relationships and patterns among them. By combining these approaches, a multilevel, multivariate model can analyze complex data structures with multiple levels and multiple outcomes, providing a more comprehensive understanding of the relationships within the data. An example multilevel, multivariate model is the Imer package in R. Surgeon may be utilized as the grouping variable for the multilevel modelling. The feature data 1020i-vii may be utilized as dependent variables 1-7 in the multivariate model.

[0080] As shown in block 1530, the method includes utilizing, by the computing system 102, model diagnostics 1620 to assess model fit, iterate model parameters, and determine the model of best fit 1630. For this process, R packages buildmer and performance were utilized to assess model validity and select optimal parameters.

[0081] Turning to FIG. 11, a flowchart shows a method of populating an insight matrix from the model. As shown in block 1710 the method includes applying, by the computing system 102, regression coefficients to the feature data, e.g., the dependent variables, determine how strongly each feature affects the phase duration. Ninety-five percent (95%) confidence intervals (CI) determine the level of certainly / uncertainly.

[0082] As shown in block 1720, the method includes generating, by the computing system 102, the insight matrix or table (TABLE 2) from the predicted effect of each feature. This step includes accounting for the surgical activities and the metadata.TABLE 2Feature Coefficient 95% CI p exp(Coefficient)(Intercept) | 2.46 | [2.03, 2.89] | < .001 | 11.7 camera out repeats 0.03 [0.02, 0.05] < .001 1.03 phase repeats | 0.17 | [0.07, 0.27] 1 0.001 | 1.19 has op field exposure 0.36 [0.05, 0.67] 0.024 1.43

[0083] As shown in block 1730 the method includes utilizing the insight matrix for guiding a surgeon using the system 1212 to optimize operation during the target phase. For example, the system 1212 may support the surgical activities of a surgeon before, during or after a next operative procedure. For example, a display device 410 (FIG. 12) in the operating room may identify as the surgeon is entering the target phase of the operation where inefficiencies are historically occurring for that surgeon or for surgeons in general, and identify more efficient options for executing the target phase.

[0084] Insights from the above table include, for example, that each camera-out (camera extraction) repetition adds three percent (3%) to phase duration. These actions provide the smallest overall impact on duration, but the model indicates the greatest certainty about this effect. Insights may further include that each phase repetition adds nineteen percent (19%) to phase duration. Insights may further include that performing an operating field exposure has the greatest impact on phase duration with forty three percent (43%) increase in duration, but an uncertainty about this effect is also the largest compared with other workflow activities.

[0085] Alternatively, the insight may be delivered in the form of a tailored optimization summary for a specific surgeon based on the data applied to the model. Such summary may be, forexample: (1) The surgeon’s vascular pedicle dissection-phase (the target phase) is above the benchmark value set by an average (as a non-limiting example) of other surgeons in the geographic region where the operation is performed. This may be because the system determines that the surgeon typically repeats the phase at least three times and each phase repetition adds seven (7) minutes to the total case duration. (2) The surgeon’s phase duration is 36% faster when the surgeon performs colon mobilization before vascular pedicle dissection. (3) The surgeon could save an average on fourteen (14) minutes per case if the surgeon does not remove the camera from patient body during vascular pedicle dissection. (4) The surgeon saves between 5-12 minutes by performing the operating field exposure phase.

[0086] FIG. 12 shows an illustration of graphically providing insights to a surgeon based on the collected and processed data. A first field 1810 on a display 1815 of a computing device 410, such as a tablet or other mobile device, may graphically show the target phase. A second field 1820 may show the phases identified from the collected data. A third field 1830 may show the insights tailored for a specific surgeon. Insights can be supplemented with other data points, such as comparisons to benchmark values. Supplementary data points can be utilized to put the insights into a wider context.

[0087] According to the embodiments, the phase detection algorithm, camera out and instrument and anatomy detection contribute to the video derived features. The outputs of the Al models that give a sequence of surgical events - the phase, anatomy in view, instrument in view and camera in / out at each second of the video - are utilized to derive the video features reference to in this disclosure by taking combinations of the Al outputs to create the disclosed novel metrics. It is to be appreciated that the video derived features are taken from the recording of the entire operational procedure.

[0088] Generally, the disclosed system and method may be utilized to calculate various types of surgical durations as well as phase durations. This could include one or more of: (i) caseduration; (ii) phase duration; (iii) camera out duration; (iv) a duration taken by a doctor to complete certain predetermined phases representing safety milestones; (v) a duration taken by a doctor to identify critical structures;; and (vi) a duration of a pause taken by a doctor between consecutive phases.

[0089] For example, in a partial nephrectomy operation, the time elapsed between Hilar Clamping and Hilar Unclamping phases is known as Warm Ischemia Time. This is a clinically meaningful measure and may have an impact on a patient outcome.

[0090] The disclosed system and method can be applied to any phase repetition and / or phase ordering, and / or phase as a feature. The disclosed system and method is not limited to the phases mentioned in this disclosure.

[0091] The disclosed system and method may utilize various types of case or surgeon metadata. The disclosed system and method is not limited to the metadata disclosed above. The metadata could include, as non-limiting examples, (i) surgeon experience; (ii) surgeon specialty; (iii) instruments utilized for the procedure; (iv) the hospital utilized for the procedure; (v) the country where the procedure is performed; and (vi) patient factors such as BMI, age, gender, medical history.

[0092] The disclosed system and method may extract features also from various videobased data, and is not limited to the features disclosed above. Such features include, as non-limiting examples, anatomy and instruments. That is, the disclosed system and method is not limited to extracting phase and camera data identified above. The disclosed system and method may, for example, extract (i) instruments detected in video feed; (ii) a duration that instruments are utilized by a doctor of in certain predetermined phases of an operation / full case (e.g., a procedure); (iii) anatomical features detected in a video feed; (iv) a duration for detecting by a doctor of anatomical features; (v) a duration taken by a doctor to identify critical anatomical structures; and (vi) a duration taken by a doctor to reach certain predetermined phases representing safety milestones.

[0093] The disclosed system and method may be utilize to derive insights post-operatively, and those insights may be utilized in other ways than as disclosed above. For example, graphical pointers may be added to the a graphical representation of a surgical timeline, where time inefficiencies are detected. If, for a given procedure, each repetition of a phase adds to the overall phase duration, phase repetitions could be highlighted with a warning sign, e.g., including a hover text stating “You performed phase X three times during this case and the overall phase duration was 25 minutes. By performing the phase in one continuous execution, you could have saved 12 minutes.”.

[0094] The insights generated by the system and method may be utilized to generate notes that may be added to a case summary in written text. For example, a case summary may include advisory text indicating, i.e., ‘You performed phase X three times during this case and the overall phase duration was 25 minutes. By performing the phase in one continuous execution, you could have saved 12 minutes.”

[0095] The insights generated by the system and method may be utilized to generate notifications that may be sent to the surgeon after performing a case procedure, noting performance inefficiencies that results in prolonging the procedure. For example, after a procedure has finished, the surgeon could be sent a notification including advisory text indicating “You performed phase X three times during this case and the overall phase duration was 25 minutes. By performing the phase in one continuous execution, you could have saved 12 minutes.”

[0096] It can be appreciated that actions such as camera out, instrument and anatomy detection, contribute to the video derived features. The embodiments are directed to running an Al model to provide a sequence of surgical events, e.g., a phase, anatomy in view, instrument in view and camera in or out at each second of the video. The sequence of events is then used to derived the video features referred to herein by taking combinations of the Al outputs to create the metrics disclosed herein.

[0097] It can be appreciated that the disclosed embodiments contribute towards developing the building blocks that are required for real-time case duration prediction. Real time predictions may enable the prediction of time for performing a surgical procedure during an operation and enabling a more accurate scheduling of procedures.

[0098] Generally, the embodiments are directed to a system and method for utilizing video derived features, combined with the utilization of metadata, to understand the features that correlate to surgical timings. The embodiments are directed to the product output of an Al model, including the utilization of the output in generating post-operative insights and case summaries. It is to be appreciated that reference herein may be to average time inefficiencies, however the disclosed system and method can also be utilized to identify inefficiencies in individual cases.

[0099] Turning to FIG. 13, other aspects of the disclosed method are shown. As shown in block 1810A, the method includes feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a target phase of a surgical operation performed on a patient by a surgeon. The surgical operation is performed over a surgical field defined on a body of the patient. The surgical operation is segmented into a plurality of phases including the targeted phase.

[0100] As shown in block 1820A, the method includes identifying feature data, by the computing system. This includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon.

[0101] As shown in block 1830A, the method includes deriving, by the computing system, result data from the feature data. The result data is indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model.

[0102] As shown in block 1840 A, deriving the result data includes applying metadatarelated to one or both of the surgeon and the operation.

[0103] As shown in block 1850A, the method includes identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

[0104] As indicated, the result data includes a duration of performing the target phase by the surgeon. The result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

[0105] As shown in block 1855 A, transmitting the alert to the surgeon (block 1850A) includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data.

[0106] As shown in block 1860A, the method includes identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

[0107] As indicated, the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performing the targeted phase by the surgeon; surgical approach applied by the surgeon; a duration of any pauses in performance of the surgical operation; andwhether the surgeon exposed the operating field during performance of the phase.

[0108] As also indicated, the metadata includes one or more of surgeon experience level; surgeon specialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location of performance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history.

[0109] As shown in block 1870A, the method includes training the model on the computing system with (i) collected image data from the video recording system representing video recordings of the target phase of a surgical operation performed by a plurality of surgeons, each of the surgeons performing the targeted phase of the operation a plurality of times; and (ii) collected metadata related to one or more of the surgeons and the surgical procedure for each performance of the surgical operation.

[0110] The processing shown in the accompanying and above described flowcharts is not intended to indicate that the operations are to be executed in any particular order or that all of the operations shown in the flowcharts are to be included in every case. Additionally, the processing shown in the flowcharts can include any suitable number of additional operations.

[0111] Turning now to FIG. 14, a computer system 1900 is generally shown in accordance with an aspect. It is to be appreciated that the methods disclosed herein can be implemented, for example, by all or a portion of CAS system 100 of FIG. 1, the data analysis system 400, and / or computer system 1900 of FIG. 14.

[0112] The computer system 1900 can be an electronic computer framework comprising and / or employing any number and combination of computing devices and networks utilizing various communication technologies, as described herein. The computer system 1900 can be easily scalable, extensible, and modular, with the ability to change to different services or reconfiguresome features independently of others. The computer system 1900 may be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer system 1900 may be a cloud computing node. Computer system 1900 may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system 1900 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media, including memory storage devices.

[0113] As shown in FIG. 14, the computer system 1900 has one or more central processing units (CPU(s)) 1901a, 1901b, 1901c, etc. (collectively or generically referred to as processor(s) 1901). The processors 1901 can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The processors 1901 can be any type of circuitry capable of executing instructions. The processors 1901, also referred to as processing circuits, are coupled via a system bus 1902 to a system memory 1903 and various other components. The system memory 1903 can include one or more memory devices, such as read-only memory (ROM) 1904 and a random-access memory (RAM) 1905. The ROM 1904 is coupled to the system bus 1902 and may include a basic input / output system (BIOS), which controls certain basic functions of the computer system 1900. The RAM is read- write memory coupled to the system bus 1902 for use by the processors 1901. The system memory 1903 provides temporary memory space for operations of said instructions during operation. The system memory 1903 can include random access memory (RAM), read-only memory, flash memory, or any other suitable memory systems.

[0114] The computer system 1900 comprises an input / output (I / O) adapter 1906 and a communications adapter 1907 coupled to the system bus 1902. The I / O adapter 1906 may be asmall computer system interface (SCSI) adapter that communicates with a hard disk 1908 and / or any other similar component. The I / O adapter 1906 and the hard disk 1908 are collectively referred to herein as a mass storage 1910.

[0115] Software 1911 for execution on the computer system 1900 may be stored in the mass storage 1910. The mass storage 1910 is an example of a tangible storage medium readable by the processors 1901, where the software 1911 is stored as instructions for execution by the processors 1901 to cause the computer system 1900 to operate, such as is described hereinbelow with respect to the various Figures. Examples of computer program product and the execution of such instruction is discussed herein in more detail. The communications adapter 1907 interconnects the system bus 1902 with a network 1912, which may be an outside network, enabling the computer system 1900 to communicate with other such systems. In one aspect, a portion of the system memory 1903 and the mass storage 1910 collectively store an operating system, which may be any appropriate operating system to coordinate the functions of the various components shown in FIG. 14.

[0116] Additional input / output devices are shown as connected to the system bus 1902 via a display adapter 1915 and an interface adapter 1916. In one aspect, the adapters 1906, 1907, 1915, and 1916 may be connected to one or more I / O buses that are connected to the system bus 1902 via an intermediate bus bridge (not shown). A display 1919 (e.g., a screen or a display monitor) is connected to the system bus 1902 by a display adapter 1915, which may include a graphics controller to improve the performance of graphics-intensive applications and a video controller. A keyboard, a mouse, a touchscreen, one or more buttons, a speaker, etc., can be interconnected to the system bus 1902 via the interface adapter 1916, which may include, for example, a Super I / O chip integrating multiple device adapters into a single integrated circuit. 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). Thus, as configured in FIG. 14, the computer system 1900 includes processing capabilityin the form of the processors 1901, and storage capability including the system memory 1903 and the mass storage 1910, input means such as the buttons, touchscreen, and output capability including the speaker 1923 and the display 1919.

[0117] In some aspects, the communications adapter 1907 can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network 1912 may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An external computing device may connect to the computer system 1900 through the network 1912. In some examples, an external computing device may be an external web server or a cloud computing node.

[0118] It is to be understood that the block diagram of FIG. 14 is not intended to indicate that the computer system 1900 is to include all of the components shown in FIG. 14. Rather, the computer system 1900 can include any appropriate fewer or additional components not illustrated in FIG. 14 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the aspects described herein with respect to computer system 1900 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. Various aspects can be combined to include two or more of the aspects described herein.

[0119] According to an aspect of the disclosure, a computer-implemented method includes: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, bythe computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model; wherein deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

[0120] According to another aspect of the disclosure directed to the computer-implemented method, the result data includes a duration of performing the target phase by the surgeon and the result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

[0121] According to another aspect of the disclosure directed to the computer-implemented method, the method includes identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

[0122] According to another aspect of the disclosure directed to the computer-implemented method, the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performing the targeted phase by the surgeon; surgical approach applied by the surgeon; a durationof any pauses in performance of the surgical operation; and whether the surgeon exposed the operating field during performance of the phase.

[0123] According to another aspect of the disclosure directed to the computer-implemented method, the metadata includes one or more of surgeon experience level; surgeon specialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location of performance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history.

[0124] According to another aspect of the disclosure directed to the computer-implemented method, transmitting the alert to the surgeon includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data.

[0125] According to another aspect of the disclosure directed to the computer-implemented method, the method includes training the model on the computing system with (i) collected image data from the video recording system representing video recordings of the target phase of a surgical operation performed by a plurality of surgeons, each of the surgeons performing the targeted phase of the operation a plurality of times; and (ii) collected metadata related to one or more of the surgeons and the surgical procedure for each performance of the surgical operation.

[0126] According to another aspect of the disclosure, a computing system includes: a data store including video data associated with a surgical procedure; and a machine learning training system configured for: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient andwherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model; wherein deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

[0127] According to another aspect of the disclosure directed to the system, the result data includes a duration of performing the target phase by the surgeon and the result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

[0128] According to another aspect of the disclosure directed to the system, the machine learning training system is further trained for: identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

[0129] According to another aspect of the disclosure directed to the system, the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performingthe targeted phase by the surgeon; surgical approach applied by the surgeon; a duration of any pauses in performance of the surgical operation; and whether the surgeon exposed the operating field during performance of the phase.

[0130] According to another aspect of the disclosure directed to the system, the metadata includes one or more of surgeon experience level; surgeon specialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location of performance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history.

[0131] According to another aspect of the disclosure directed to the system, transmitting the alert to the surgeon includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data.

[0132] According to another aspect of the disclosure, a computer program product including a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors of a computing system to perform a plurality of operations including: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the featuredata, the result data being indicative of one or more of an operation trend and an operation inefficiency the surgeon relative to other surgeons for which data was collected and utilized for training the model; wherein deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

[0133] According to another aspect of the disclosure directed to the computer program product, the result data includes a duration of performing the target phase by the surgeon and the result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

[0134] According to another aspect of the disclosure directed to the computer program product, the program product further causes the one or more processors to perform further operations including identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

[0135] According to another aspect of the disclosure directed to the computer program product, the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performing the targeted phase by the surgeon; surgical approach applied by the surgeon; a duration of any pauses in performance of the surgical operation; and whether the surgeon exposed the operating field during performance of the phase.

[0136] According to another aspect of the disclosure directed to the computer program product, the metadata includes one or more of surgeon experience level; surgeon specialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location of performance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history.

[0137] According to another aspect of the disclosure directed to the computer program product, the program product further causes the one or more processors to perform further operations including transmitting the alert to the surgeon includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data.

[0138] According to another aspect of the disclosure directed to the computer program product, the program product further causes the one or more processors to perform further operations including training the model on the computing system with (i) collected image data from the video recording system representing video recordings of the target phase of a surgical operation performed by a plurality of surgeons, each of the surgeons performing the targeted phase of the operation a plurality of times; and (ii) collected metadata related to one or more of the surgeons and the surgical procedure for each performance of the surgical operation.

[0139] Aspects disclosed herein 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 various aspects.

[0140] 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 storagemedium 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.

[0141] 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.

[0142] Computer-readable program instructions for carrying out operations of the present disclosure 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 anycombination of one or more programming languages, including an object-oriented programming language, such as Smalltalk, C++, high-level languages such as Python, 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 disclosure.

[0143] Aspects 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 disclosure. 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.

[0144] These computer-readable program instructions may be provided to a processor of a computer system, 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 thecomputer-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.

[0145] 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.

[0146] 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. 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.

[0147] The descriptions of the various aspects 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 bestexplain 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.

[0148] Various aspects are described herein with reference to the related drawings. Alternative aspects can be devised without departing from the scope of this disclosure. 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 disclosure 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.

[0149] 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.

[0150] 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 integernumber 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.”

[0151] 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.

[0152] For the sake of brevity, conventional techniques related to making and using aspects 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.

[0153] 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.

[0154] 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).

[0155] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), graphics processing units (GPUs), 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

CLAIMSWhat is claimed is:

1. A computer-implemented method comprising: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model; wherein deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

2. The computer-implemented method of claim 1 , wherein the result data includes a duration of performing the target phase by the surgeon and the result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

3. The computer-implemented method of claim 1 or 2, including identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

4. The computer-implemented method of any of claims 1-3, wherein the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performing the targeted phase by the surgeon; surgical approach applied by the surgeon; a duration of any pauses in performance of the surgical operation; and whether the surgeon exposed the operating field during performance of the phase.

5. The computer-implemented method of any of claims 1-4, wherein: the metadata includes one or more of surgeon experience level; surgeon specialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location of performance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history; or transmitting the alert to the surgeon includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data; orthe method comprises training the model on the computing system with (i) collected image data from the video recording system representing video recordings of the target phase of a surgical operation performed by a plurality of surgeons, each of the surgeons performing the targeted phase of the operation a plurality of times; and (ii) collected metadata related to one or more of the surgeons and the surgical procedure for each performance of the surgical operation.

6. A computing system comprising: a data store comprising video data associated with a surgical procedure; and a machine learning training system configured for: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency of the surgeon relative to other surgeons for which data was collected and utilized for training the model; and identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data, to thereby optimize performance of the surgeon during the target phase.

7. The system of claim 6, wherein: the result data includes a duration of performing the target phase by the surgeon and the result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

8. The system of claim 6 or 7, wherein the machine learning training system is further trained for: identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

9. The system of any of claims 6-8, wherein the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performing the targeted phase by the surgeon; surgical approach applied by the surgeon; a duration of any pauses in performance of the surgical operation; and whether the surgeon exposed the operating field during performance of the phase.

10. The system of any of claims 6-9, wherein: deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and the metadata includes one or more of surgeon experience level; surgeon specialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location ofperformance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history; or transmitting the alert to the surgeon includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data.

11. A computer program product comprising a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors of a computing system to perform a plurality of operations comprising: feeding a model on a computing system with image data collected from a video recording system, including a camera, representing video recordings of a surgical operation, including a target phase of the surgical operation, performed on a patient by a surgeon, wherein the surgical operation is performed over a surgical field defined on a body of the patient and wherein the surgical operation is segmented into a plurality of phases including the targeted phase; identifying feature data, by the computing system, which includes identifying by the computing system from the image data, activity data of surgical activities performed during the surgical operation by the surgeon; deriving, by the computing system, result data from the feature data, the result data being indicative of one or more of an operation trend and an operation inefficiency the surgeon relative to other surgeons for which data was collected and utilized for training the model; and utilizing the result data to optimize performance of the surgeon during the target phase.

12. The computer program product of claim 11, wherein:utilizing the result data to optimize performance of the surgeon during the target phase includes identifying and displaying on a display or transmitting an alert to a surgeon, by the computing system, the result data; and the result data includes a duration of performing the target phase by the surgeon and the result data identifies inefficiencies in performing the targeted phase that results in increasing the duration of completing the target phase beyond a targeted threshold.

13. The computer program product of claim 11 or 12, further causing the one or more processors to perform further operations comprising identifying inefficiencies by comparing the duration of completing the target phase with (i) an average duration of completing the targeted phase relative to the other surgeons for which data was collected and utilized for training the model; or (ii) an average duration of completing the targeted phase relative to the same surgeon during prior surgical operations; or (iii) predetermined inefficiencies for the targeted phase.

14. The computer program product of any of claims 11-13, wherein the activity data represents one or more of: duration for a surgeon to complete the surgical operation; repetitions of the surgeon of removing the camera from the operating field; duration of completion of the targeted phase by the surgeon; duration to achieve one or more predetermined surgical milestones; duration of a surgeon to identify one or more critical structures; repetition of performing the targeted phase by the surgeon; surgical approach applied by the surgeon; a duration of any pauses in performance of the surgical operation; and whether the surgeon exposed the operating field during performance of the phase.

15. The computer program product of any of claims 11-14, wherein: deriving the result data includes applying metadata related to one or both of the surgeon and the operation; and the metadata includes one or more of surgeon experience level; surgeonspecialty; instrumentation utilized by the surgeon; a hospital in which the surgical operation is performed; a country in which the surgical operation is performed; geographic location of performance of the surgical operation; or one or more patient characteristics including patient body mass index, patient age; patient gender and patient medical history; or transmitting the alert to the surgeon includes transmitting an electronic communication to the surgeon identifying a potential duration of performing the target phase by the surgeon that is shorter than an actual recorded duration of performing the phase by the surgeon if the surgeon where to implement corrective measures identified by the system from the result data; or the computer program product further causes the one or more processors to perform further operations comprising training the model on the computing system with (i) collected image data from the video recording system representing video recordings of the target phase of a surgical operation performed by a plurality of surgeons, each of the surgeons performing the targeted phase of the operation a plurality of times; and (ii) collected metadata related to one or more of the surgeons and the surgical procedure for each performance of the surgical operation.

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