Surgical standardization metrics for surgical workflow variation

Abstract

An aspect includes generation of a surgical standardization metric for surgical workflow variation. The surgical standardization metric can be a distance-based metric that summarizes variability of a set of surgical workflows for a surgical procedure. Determination of the distance-based metric can be customized through one or more user interfaces. The surgical standardization metric can provide a compressed similarity indicator for comparison across multiple data sets, generation of progress indication over time, and / or reduced storage of summarization data.

Description

SURGICAL STANDARDIZATION METRICS FOR SURGICAL WORKFLOWVARIATIONCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 655,117, filed June 3, 2024, the entire content of which is incorporated herein by reference.BACKGROUND

[0002] The present disclosure relates in general to computing technology and relates more particularly to computing technology for surgical standardization metrics for surgical workflow variation.

[0003] 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.

[0004] Alternatively, or in addition, the video data can be stored and / or transmitted for several purposes such as archival, training, post-surgery analysis, and / or patient consultation. The process of analyzing and comparing a large amount of video data from multiple surgical procedures to identify commonalities can be highly subjective and error- prone due, for example, to the volume of data and the numerous factors (e.g., patient condition, physician preferences, etc.) that impact the workflow of each individual surgical procedure that is being analyzed.SUMMARY

[0005] According to an aspect, a system includes a memory device and one or more processors coupled with the memory device. The one or more processors are configured to display a user interface that provides user configurability of a principal workflow and one or more standardization customizations for a surgical procedure and access a set of surgical workflows comprising a plurality of phases in sequences of previousperformances of the surgical procedure. The one or more processors are further configured to determine a distance-based metric that summarizes variability of the set of surgical workflows using the one or more standardization customizations and display a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0006] According to another aspect, a computer-implemented method for surgical standardization metric generation for surgical workflow variation includes displaying a user interface that provides user configurability of a principal workflow for a surgical procedure, accessing a set of surgical workflows comprising a plurality of phases in sequences of previous performances of the surgical procedure, determining a distancebased metric that summarizes variability of the set of surgical workflows using one or more standardization customizations, and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0007] According to a further aspect, a computer program product includes a memory device with computer readable instructions stored thereon, where executing the computer readable instructions by one or more processing units causes the one or more processing units to perform a plurality of operations. The operations include displaying a user interface that provides user configurability of a principal workflow for a surgical procedure, accessing a set of surgical workflows comprising a plurality previous performances of the surgical procedure, determining a distance-based metric that summarizes variability of the set of surgical workflows, and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0008] Additional technical features and benefits are realized through the techniques of the present invention. Aspects of the invention are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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 foregoingand other features and advantages of the aspects of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0010] FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects;

[0011] FIG. 2 depicts a surgical procedure system according to one or more aspects;

[0012] FIG. 3 depicts a system for analyzing video captured by a video recording system according to one or more aspects;

[0013] FIG. 4 depicts a user interface for adaption customization according to one or more aspects;

[0014] FIG. 5 depicts a custom workflow creation user interface according to one or more aspects;

[0015] FIG. 6 depicts a user interface for comparisons according to one or more aspects;

[0016] FIG. 7 depicts a user interface for progression over time according to one or more aspects;

[0017] FIG. 8 depicts a process for pairwise distance determination for surgical workflows according to one or more aspects;

[0018] FIG. 9 depicts a process for distance determination for surgical workflows relative to a target workflow according to one or more aspects;

[0019] FIG. 10 depicts a process for edit distance determination according to one or more aspects;

[0020] FIG. 11 depicts a function of cost versus time of phase according to one or more aspects;

[0021] FIG. 12 depicts an example of a workflow resolution adjustment according to one or more aspects;

[0022] FIG. 13 depicts an example of workflow alignment to a standardized workflow according to one or more aspects;

[0023] FIG. 14 depicts a process of surgical standardization metrics determination for surgical workflow variation according to one or more aspects; and

[0024] FIG. 15 depicts a computer system according to one or more aspects.

[0025] 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 ofthe invention. For instance, the actions can be performed in a differing order, or actions can be added, deleted, or modified. Also, the term “coupled” and variations thereof describe having a communications path between two elements and do not imply a direct connection between the elements with no intervening elements / connections between them. All of these variations are considered a part of the specification.DETAILED DESCRIPTION

[0026] Aspects of the technical solutions described herein relate to analyzing surgical workflow data to generate surgical standardization metrics for surgical workflow variation determination.

[0027] Exemplary aspects of the technical solutions described herein include systems and methods for surgical workflow comparisons to distinguish between variations that may indicate a need to change aspects of surgical procedures going forward.

[0028] Quantifying variation of surgical workflows can be useful to compare sites, surgeons or procedures. Such comparisons may indicate whether retraining or proctoring is necessary to reduce variation or may establish how closely surgical workflows are at following a baseline proctored workflow. Quantifying the amount of variation in a set of surgical workflows can be based on a determination of similarity between workflows. For instance, if all workflows are very similar, standardization is high, whereas when workflows are dissimilar, standardization is low. Through the quantification of variation (or similarity), complex workflow information can be compressed into a single number which can be interpreted and tracked through time. Once metrics are determined, computational burden can be reduced and system responsiveness increased, as the compressed metrics are tracked and shared with other systems to avoid repeated analysis and possible comparison of dissimilar information. Further, rather than sharing larger datasets and video files, the metrics can be shared, which can also reduce network and storage demands.

[0029] In exemplary aspects of the technical solutions described herein, surgical data is captured by a computer-assisted surgical (CAS) system, segmented into surgical phases, and corresponding surgical workflows are established. Aspects summarize standardization of the surgical workflows to understand deviations through metrics that compress the results for display, comparison, storage, and retrieval.

[0030] Phase segmentation data can be used to build a process model of a surgical procedure. A process model can be formed as a graph of transitions between surgical phase. One approach to metrics for variation summarization is a modified edit distance. For example, edit distance between surgical workflows can include extensions, such as phase repetitions, where toggling and phase repetition between a set of surgical phases can be an indicator of case complexity or surgeon skill rather than a deviation from a preferred surgical workflow. Repeats of a phase can have a lower cost than adding a new phase.

[0031] Another extension can include optional phases, where certain surgical phases may only be performed depending on the patient presented or may be optional for other reasons. Inserting or removing these phases can have a low or zero cost.

[0032] A further extension can include typical durations, where the cost of a phase which typically takes a longer time can be higher than the cost of a phase which is typically shorter. Duration of return, where returning to a phase for a short time can have a lower cost than returning to a phase for a long time as another extension. With regard to subsequences, some phases almost always follow each other, forming common subsequences. Adding or deleting such subsequences can have less cost than adding each phase in the subsequence separately. Where a phase occurs within a surgical procedure can be relevant with respect to time or location of phases in a workflow sequence. A phase occurring later in an operation may have less significance than the same phase occurring in early stages with regard to time or location of phase. Phases immediately before and after an edited phase can provide useful context. For instance, some phase transitions are very unlikely. For example, if it is known that transition A to X is very unlikely, and an insertion AC to AXC is detected, the insertion can have a higher cost.Another way of encapsulating duration information is to create new workflows with phase repeats based on the duration of those phases. Further, workflow phases can be aligned to a standardized surgical workflow using a standard phase edit method, and a deviation of phase duration differences can be determined. Such extensions can allow assessment of standardization to proctored workflows, or understand variation within a set of workflows in a more realistic way, thereby enhancing analysis of surgical workflows. This can provide value to users by quantifying how standardization of the users improves through time, compares to other institutions or changes following retraining or proctoring.

[0033] 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. 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.

[0034] 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).

[0035] The video recording system 104 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, and / or the like including combinations and / or multiples thereof. The cameras 105 capture video data of the surgical procedure being performed. The video recording system 104 includes one or more video capture devices that can include cameras 105 placed in the surgical room to capture events surrounding (i.e., outside) the patient being operated upon. The video recording system 104 further includes cameras 105 that are passed inside (e.g., endoscopic cameras) the patient 110 to capture endoscopic data. The endoscopic data provides video and images of the surgical procedure.

[0036] 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 1500 of FIG. 15. 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.

[0037] A data collection system 150 can be employed to store the surgical data, including the video(s) captured during the surgical procedures. The data collection system 150 includes one or more storage devices 152. The data collection system 150 can be a local storage system, a cloud-based storage system, or a combination thereof. Further, the data collection system 150 can use any type of cloud-based storage architecture, for example, public cloud, private cloud, hybrid cloud, and / or the like including combinations and / or multiples thereof. In some examples, the data collection system can use a distributed storage, i.e., the storage devices 152 are located at different geographic locations. The storage devices 152 can include any type of electronic data storage media used for recording machine-readable data, such as semiconductor-based, magnetic-based, optical-based storage media, and / or the like including combinations and / or multiples thereof. For example, the data storage media can include flash-based solid-state drives (SSDs), magnetic-based hard disk drives, magnetic tape, optical discs, and / or the like including combinations and / or multiples thereof.

[0038] 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 alreadystored / being stored in the data collection system 150. 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.

[0039] In one or more examples, the video captured by the video recording system 104 is stored on the data collection system 150. In some examples, the computing system 102 curates parts of the video data being stored on the data collection system 150. In some examples, the computing system 102 filters the video captured by the video recording system 104 before it is stored on the data collection system 150. Alternatively, or in addition, the computing system 102 filters the video captured by the video recording system 104 after it is stored on the data collection system 150. Instrument data (e.g., robotic logs, electrosurgical instrument logs, etc.) can also be stored in the data collection system 150.

[0040] A surgical data management system 160 can provide access to portions of data captured in the data collection system 150, as well as data and records stored in other systems. The surgical data management system 160 can establish user access permissions to patient and surgical data. The surgical data management system 160 can also control access through an interface based on the user access permissions. Access to the surgical data management system 160 can be provided through one or more applications or secure web pages. The surgical data management system 160 can be a stand-alone application, module, and / or an extension of another system. Additional aspects of the surgical data management system 160 can include accessing artificial intelligence (Al)-powered surgical video and analytics. Further aspects of the surgical data management system 160 can include accessing simulation materials that can assist surgeons to prepare, practice, and teach surgical procedures. Further aspects of the surgical data management system 160 can include integrating aspects of equipment in an operating room, surgery planning, rating surgeon performance, and other such features. The surgical data management system 160 can also provide access to technical specifications and information relating to the use of surgical instruments, for example.

[0041] 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 ormore 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.

[0042] The surgical procedure support system 202 can also communicate with other systems through a network 230. For example, the surgical procedure support system 202 can communicate with a surgical workflow variation visualizer 240 and a surgical data post-processing system 250 through the network 230. Other types of devices, such as a computing device 234 (e.g., a mobile phone, laptop, personal computer, or tablet computer), can communicate directly with the surgical procedure support system 202 or through the network 230. As one example, user interfaces 210 may be connected to or integrated with the surgical procedure support system 202 by a wired connection while the computing device 234 connects to the surgical procedure support system 202 via a wireless connection. In some aspects, the computing device 234 can execute or link to another computer system that executes the surgical data management system 160 of FIG. 1 to access various data sources through the network 230.

[0043] The surgical data post-processing system 250 can receive surgical data and associated data generated by the surgical procedure support system 202 and may be separately stored and secured through other data storage. Access to specific data or portions of data through the surgical data post-processing system 250 may be limited by associated permissions. The surgical data post-processing system 250 may include features such as video viewing, video sharing, data analytics, and selective data extraction.

[0044] The surgical workflow variation visualizer 240 can provide viewing access to surgical workflows and associated data sources, such as surgical data 214, data collected in the one or more storage devices 152 of FIG. 1, and post-processed data generated by thesurgical data post-processing system 250 that is associated with the surgical workflows. For example, surgical instrument data, video data, and artificial intelligence generated data can be accessed for viewing through the surgical workflow variation visualizer 240 to view context information associated with nodes of a process model of surgical workflows. The surgical data post-processing system 250 may generate surgical performance metrics and comparison data across data sets collected at multiple locations, making analytics data available to the surgical workflow variation visualizer 240. The surgical workflow variation visualizer 240 can load and analyze surgical workflow data of a user, for example, when the user performs a login or otherwise activates the surgical workflow variation visualizer 240. In some aspects, the surgical workflow variation visualizer 240 can present surgical workflows as a flow diagram of a process model associated with workflows of a surgical procedure, for instance, as selected by a user through a user interface. The flow diagram can be modified to resize one or more nodes to draw attention to portions that have a higher level of entropy. Other types of highlighting and modification of the flow diagram can be provided as furth described herein. In some aspects, the surgical workflow variation visualizer 240 and / or surgical data postprocessing system 250 can be components of the surgical data management system 160 of FIG. 1.

[0045] One or more computing device 264 (e.g., a mobile phone, laptop, personal computer, or tablet computer), can execute the surgical data management system 160 of FIG. 1 to access various data sources through a network 260. The network 230 may be within a facility or multiple facilities maintained within a private network. The network 260 may be a wider area network, such as the internet. Accordingly, the networks 230 and 260 may have access to different files and data sets along with shared access to select files and data sets. In some aspects, networks 230 and 260 can be combined.

[0046] 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, etc.) 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 300uses data streams in the surgical data to identify procedural states according to some aspects.

[0047] System 300 includes a data reception system 305 that collects surgical data, including the video data and surgical instrumentation data. The data reception system 305 can include one or more devices (e.g., one or more user devices and / or servers) located within and / or associated with a surgical operating room and / or control center. The data reception system 305 can receive surgical data in real-time, i.e., as the surgical procedure is being performed. Alternatively, or in addition, the data reception system 305 can receive or access surgical data in an offline manner, for example, by accessing data that is stored in the data collection system 150 of FIG. 1.

[0048] 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, etc., 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 in the cloud 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.

[0049] 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 separatefrom devices that “infer,” i.e., perform real-time processing of surgical data using the trained machine learning models 330.

[0050] Machine learning processing system 310, in some examples, further includes a data generator 315 to generate simulated surgical data, such as a set of virtual images, or record the video data from the video recording system 104, to train the 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, etc.) during the surgery, anon-wearable imaging device located within an operating room, or an endoscopic camera inserted inside the patient 110 of FIG. 1. 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.

[0051] Each of the images and / or videos recorded in the data store 320 for training 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, etc.). Further, the other data can include image-segmentation data that identifies and / or characterizes one or more objects (e.g., tools, anatomical objects, etc.) that are depicted in the image or video. The characterization can indicate the position, orientation, or pose of the object in the image. For example, the characterization can indicate a set of pixels that correspond to the object and / or a state of the object resulting from a past or current user handling. Localization can be performed using a variety of techniques for identifying objects in one or more coordinate systems.

[0052] The machine learning training system 325 uses the recorded data in the data store 320, which can include the simulated surgical data (e.g., set of virtual images) andactual surgical data to train the machine learning models 330. The machine learning model 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 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 a trained machine learning model 330 using a specific data structure for that trained machine learning model 330. The data structure can also include one or more non-leamable variables (e.g., hyperparameters and / or model definitions).

[0053] Machine learning execution system 340 can access the data structure(s) of the machine learning models 330 and accordingly configure the machine learning models 330 for inference (i.e., prediction). The 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 machine learning models 330 can be indicated in the corresponding data structures. The machine learning model 330 can be configured in accordance with one or more hyperparameters and the set of learned parameters.

[0054] The 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 offlinemanner from the data collection system 150 or from any other data source (e.g., local or remote storage device).

[0055] 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, etc., 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.

[0056] The machine learning models 330, once trained, can analyze the input surgical data, and in one or more aspects, predict and / or characterize features (e.g., structures) included in the video data included with the surgical data. The video data can include sequential images and / or encoded video data (e.g., using digital video file / stream formats and / or codecs, such as MP4, MOV, AVI, WEBM, AVCHD, OGG, etc.). 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 machine learning models include or are associated with a preprocessing or augmentation (e.g., intensity normalization, resizing, cropping, etc.) that is performed prior to segmenting the video data. An output of the one or more machine learning models can include image-segmentation or probabilistic heatmap data that indicates which (if any) of a defined set of structures are predicted within the video data, a location and / or position and / or pose of the structure(s) within the video data, and / or state of the structure(s). The location can be a set of coordinates in an image / frame in the video data. For example, the coordinates can provide a bounding box. The coordinates can provide boundaries that surround the structure(s) being predicted. The 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.

[0057] 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 phaseprediction can be used without affecting the aspects of the technical solutions described herein. In some examples, the machine learning processing system 310 includes a phase detector 350 that uses the machine learning models to identify a phase within the surgical procedure (“procedure”). Phase detector 350 uses a particular procedural tracking data structure 355 from a list of procedural tracking data structures. Phase detector 350 selects the procedural tracking data structure 355 based on the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure is predetermined or input by actor 112. The procedural tracking data structure 355 identifies a set of potential phases that can correspond to a part of the specific type of procedure.

[0058] 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, etc.), pre-condition (e.g., lesions, polyps, etc.). In some examples, the machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, etc.

[0059] 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 availed 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), etc. Thus, phase detector 350 can use the segmented data generated by machine learning execution system 340 that indicates the presence and / or characteristics of particular objects within a field of view to identify an estimated node to which the real image data corresponds. Identification of the node (i.e.,phase) can further be based upon previously detected phases for a given procedural iteration and / or other detected input (e.g., verbal audio data that includes person-to-person requests or comments, explicit identifications of a current or past phase, information requests, etc.).

[0060] The phase detector 350 outputs the phase prediction associated with a portion of the video data that is analyzed by the machine learning processing system 310. The phase prediction is associated with the portion of the video data by identifying a start time and an end time of the portion of the video that is analyzed by the machine learning execution system 340. The phase prediction that is output can include segments of the video where each segment corresponds to and includes an identity of a surgical phase as detected by the phase detector 350 based on the output of the machine learning execution system 340. Further, the phase prediction, in one or more examples, can include additional data dimensions such as, but not limited to, identities of the structures (e.g., instrument, anatomy, etc.) 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.

[0061] It should be noted that although some of the drawings depict endoscopic videos being analyzed, the technical solutions described herein can be applied to analyze video and image data captured by cameras that are not endoscopic (i.e. , cameras external to the patient’s body) when performing open surgeries (i.e., not laparoscopic surgeries). For example, the video and image data can be captured by cameras that are mounted on one or more personnel in the operating room, e.g., surgeon. Alternatively, or in addition, the cameras can be mounted on surgical instruments, walls, or other locations in the operating room.

[0062] Turning now to FIG. 4, a user interface 400 for adaption customization is depicted according to one or more aspects. The user interface 400 of the surgical workflow variation visualizer 240 of FIG. 2 can provide user configurability of a principal workflow and one or more standardization customizations for a surgical procedure. A define principal workflow input 402 can allow a user to select a default workflow as the principal workflow or select an alternate workflow as the principal workflow. For instance, a default workflow may represent a surgical workflow from a proctored sourceindicating a sequence of phases of the surgical procedure. A department may have a preferred variation, for instance, based on the surgical tools available for the surgical procedure. A customize standardization input 404 can allow a user input preferences regarding how much relative weight or cost should be given when certain scenarios are detected in a surgical workflow. For example, cost or weight values may be discounted based on detection of phase toggling (e.g., returning to previous phases in the same surgical procedure), discounted for patient specific phases (e.g., an expected deviation to accommodate unique aspects of a patient), include an adjustment for phase duration, penalize for rare transitions (e.g., change between phases in a sequence that rarely occurs as compared to a larger dataset), and / or other such adjustments. Changes to standardizations and workflows can be saved for future use or to share with others, such as members of a department. The user interface 400 can also support further customizations through a opening a custom workflow creation user interface 500 of FIG. 5.

[0063] FIG. 5 depicts the custom workflow creation user interface 500 according to one or more aspects. In the example of FIG. 5, a flow diagram 502 of a proposed workflow can be depicted. Contents of the flow diagram 502 can be modified through controls 504 and / or other user inputs (e.g., clicking, tapping, dragging, dropping) to add or remove phases as well as change or rearrange phases in the flow diagram. In some aspects, the custom workflow creation user interface 500 may provide suggestion for subsequences and / or display a palette of phases and / or subsequences associated with the surgical procedure. Once edits to the flow diagram 502 are complete, the surgical workflow variation visualizer 240 of FIG. 2 can return control back to the user interface 400, where the new or edited workflow can be available for selection as the principal workflow.

[0064] FIG. 6 depicts a user interface 600 for comparisons according to one or more aspects. The surgical workflow variation visualizer 240 of FIG. 2 can generate and display the user interface 600 based on the principal workflow selected and available surgical workflow data associated with a selected comparison group, such as variability between one or more of: multiple surgeons in a same department, multiple surgeons between departments, and a same surgeon performing multiple surgeries. In the example of FIG. 6, the user interface 600 can display a most common workflow identified in a set of surgical workflows and indicate a level of variation with respect to the most common workflow for a surgeon or group of surgeons. The information can be visualized as flowdiagrams 602 and / or various plots 604. For example, text information can summarize the visualizations and performance across various sites can be summarized through the one or more surgical standardization metrics. The surgical workflow variation visualizer 240 can determine the one or more surgical standardization metrics, which can include a distancebased metric that summarizes variability of a set of surgical workflows using one or more standardization customizations.

[0065] FIG. 7 depicts a user interface 700 for progression over time according to one or more aspects. The surgical workflow variation visualizer 240 of FIG. 2 can generate and display the user interface 700. For example, the surgical workflow variation visualizer 240 can group a set of surgical workflows occurring over a period of time as a plurality of subsets, determine a distance-based metric that summarizes variability of the subsets, and output a visualization of variability of the subsets on the user interface 700. The visualization of variability of the subsets can be a scatter plot 702 of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences. For instance, the one or more flow diagrams of surgical workflow sequences can include a most common surgical workflow 704 and a surgical workflow 706 that was least similar to the most common surgical workflow.

[0066] The user interface 700 can assist in monitoring of progress toward the principal workflow over time. Deviations can trigger a need for training or further evaluation of the variations.

[0067] FIG. 8 depicts a process 800 for pairwise distance determination for surgical workflows according to one or more aspects. The process 800 can be performed by the surgical workflow variation visualizer 240 of FIG. 2 as one approach to determining the distance-based metric. In the example of FIG. 8, a set of surgical workflows 802 can include multiple sequences of phases of a surgical procedure. The surgical workflow variation visualizer 240 can calculate pairwise distances 804 for distances (e.g., differences) between each sequence of phases in the set of surgical workflows 802. Distances can be quantified by how many changes are needed to transform one sequence into another. For example, changes such as insertions, deletions, and substitutions using the smallest number of changes can be used for distance determination. Each change can have an associated cost which can be used to find the smallest number of changes. Other variations may consider factors such as a longest common sequence, insertion and deletiononly, substitution only, and / or transposition. The distance-based metric can be determined using pairwise distances for the set of surgical workflows 802 and summarized by aggregating the pairwise distances into a single ensemble measure or a multi-dimensional distance measure (e.g., a distance matrix).

[0068] FIG. 9 depicts a process 900 for distance determination for surgical workflows relative to a target workflow according to one or more aspects. The process 900 can be performed by the surgical workflow variation visualizer 240 of FIG. 2. In the example of FIG. 9, a set of surgical workflows 902 can include multiple sequences of phases of a surgical procedure. The surgical workflow variation visualizer 240 can calculate distances 904 (e.g., differences) between each sequence of phases in the set of surgical workflows 902 and a target workflow 906. The target workflow 906 can be a proctored workflow and / or a currently selected version of the principal workflow. Distance determinations can be performed using a similar approach as described with respect to FIG. 8 with respect to the target workflow 906 rather than a full pairwise comparison. The distance-based metric can be determined using distances for the set of surgical workflows 902 and summarized by aggregating the distances into a single ensemble measure or a multi-dimensional distance measure (e.g., a distance matrix).

[0069] FIG. 10 depicts a process 1000 for edit distance determination according to one or more aspects. The process 1000 can be performed by the surgical workflow variation visualizer 240 of FIG. 2. In the example of FIG. 10, edit distance determination can include using a scoring matrix to determine an optimum path of substitutions 1002, deletions 1004, and insertions 1006 between two sequences. The scoring matrix determination can also include costs for inserting a subsequence 1008, which may have a different cost value (e.g., lower) than inserting single elements, such as single phases. For instance, an insertion of a subsequence “BC” may have a lower cost (e.g., cost < 2) than inserting “B” and then inserting “C” (e.g., cost = 2). After computing a scoring matrix for possible paths, a minimum cost path can be identified.

[0070] FIG. 11 depicts a function 1100 of cost versus time of phase according to one or more aspects. The function 1100 is a cost-time relationship that can be used by the surgical workflow variation visualizer 240 of FIG. 2 to adjust cost values with respect to time. In the example of FIG. 11, If a phase (e.g., phase x) already exists in a workflow, the cost of the phase may be considered higher early in the workflow and may be less laterin the workflow. Here, time can be relative (e.g., 75% complete) or absolute (e.g., after 1 hour). This time to cost adjustment can be combined with other approaches and may be selectable as one of the customizations through the user interface 400 of FIG. 4.

[0071] A further modification can include a likelihood of transition. For instance, phases immediately before and after an edited phase can provide context. Some phase transitions may be very unlikely. For example, if it is known that transition A X is very unlikely, and an insertion AB —> AXC is detected, the insertion can have a higher cost. A transition matrix can be used to establish customized transition costs.

[0072] FIG. 12 depicts an example process 1200 of a workflow resolution adjustment according to one or more aspects. Duration information can be encapsulated to create new workflows with phase repeats based on the duration of phases. For example, for each workflow, anew workflow can be created at a lower resolution 1202 (e.g., 10 minutes per phase) or a higher resolution 1204 (e.g., 1 minute per phase), and there is a choice to be made about how the conversion is done. The result is a set of (much longer) workflows, which can be compared using a phase edit method, which then includes deviation of phase duration by default. As one example, a workflow can be split into equally timed segments. This may lose very small phases but retains duration information. The phase of a segment can be set to the majority phase of the segment. Each phase can be split into segments, then joined. This may over or under-estimate durations but retains all phases. The number of segments of a phase can be determined by duration divided by the resolution, for example.

[0073] FIG. 13 depicts an example of process 1300 workflow alignment to a standardized workflow according to one or more aspects. Another approach to calculating the optimal edit distance with weights is to align all workflow phases to a standardized surgical workflow, such as the principal workflow, using a phase edit method, and calculate a deviation of phase duration differences. Sets of surgical workflows 1302 can be compared to a standardized workflow 1304, with the results aligned as aligned workflows 1306. As an example, a proctored workflow can be ABCD, while a workflow can be ABDE. In this example, an edit distance to the proctored workflow is 2. A duration distance can be the sum of differences of durations of aligned phases. The total distance in general can be a function of edit distance and duration distance.

[0074] Turning now to FIG. 14, a flowchart of a method 1400 for surgical standardization metrics determination for surgical workflow variation is generally shown in accordance with one or more aspects. All or a portion of method 1400 can be implemented, for example, by all or a portion of CAS system 100 of FIG. 1, the system 200 of FIG. 2, and / or computer system 1500 of FIG. 15, for instance through execution of the surgical data management system 160.

[0075] At block 1402, a surgical workflow variation visualizer 240 can display a user interface 400 that provides user configurability of a principal workflow and one or more standardization customizations for a surgical procedure. At block 1404, the surgical workflow variation visualizer 240 can access a set of surgical workflows including a plurality of phases in sequences of previous performances of a surgical procedure. At block 1406, the surgical workflow variation visualizer 240 can determine a distance-based metric that summarizes variability of the set of surgical workflows using the one or more standardization customizations. At block 1408, the surgical workflow variation visualizer 240 can display a summary of surgical performance results including an indication of variability with respect to the principal workflow based on the distance-based metric.

[0076] In some aspects, the phases can be determined based on video of multiple surgical cases of the surgical procedure.

[0077] In some aspects, variability can be determined between one or more of: multiple surgeons in a same department, multiple surgeons between departments, and a same surgeon performing multiple surgeries.

[0078] In some aspects, the set of surgical workflows occurring over a period of time can be grouped as a plurality of subsets. The distance-based metric can be determined that summarizes variability of the subsets. A visualization of variability of the subsets can be output.

[0079] In some aspects, the visualization of variability of the subsets can include a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences.

[0080] In some aspects, the one or more flow diagrams of surgical workflow sequences can include a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

[0081] In some aspects, the distance-based metric can be determined using pairwise distances for the set of surgical workflows and summarized by aggregating the pairwise distances into a single ensemble measure or a multi-dimensional distance measure.

[0082] In some aspects, the principal workflow can be a target workflow, and the distance-based metric can be determined using distances for the set of surgical workflows to the target workflow and summarized by aggregating the distances into a single ensemble measure or a multi-dimensional distance measure.

[0083] In some aspects, the one or more standardization customizations can change a weighting of distance values in determining the distance-based metric.

[0084] In some aspects, the one or more standardization customizations can include one or more user-selectable adjustments to the weighting of distance values based on one or more of: phase toggling, phase repetition, optional phases, patient specific phases, phase duration, and rare transitions.

[0085] In some aspects, the distance-based metric can be a modified edit distance that is adjusted based on one or more of: phase repetition in a same surgical workflow, optional phases identified in the phases, surgical phase duration relative to a typical phase duration, duration returning to previously performed phase in the same surgical workflow, occurrence of a predetermined subsequence of the phases, phase occurrence relative to a beginning and end of the surgical procedure, phase transition likelihood, use of discretization of the phases, and deviation duration from the principal workflow.

[0086] In some aspects, a custom workflow creation user interface can be displayed and a definition of the principal workflow can be changed based on input received through the custom workflow creation user interface.

[0087] The processing shown in FIG. 14 is not intended to indicate that the operations are to be executed in any particular order or that all of the operations shown in FIG. 14 are to be included in every case. Additionally, the processing shown in FIG. 14 can include any suitable number of additional operations.

[0088] Turning now to FIG. 15, a computer system 1500 is generally shown in accordance with an aspect. The computer system 1500 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 1500 can be easily scalable, extensible, and modular, with the abilityto change to different services or reconfigure some features independently of others. The computer system 1500 may be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer system 1500 may be a cloud computing node. Computer system 1500 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 1500 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.

[0089] As shown in FIG. 15, the computer system 1500 has one or more central processing units (CPU(s)) 1501a, 1501b, 1501c, etc. (collectively or generically referred to as processor(s) 1501). The processors 1501 can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The processors 1501 can be any type of circuitry capable of executing instructions. The processors 1501, also referred to as processing circuits, are coupled via a system bus 1502 to a system memory 1503 and various other components. The system memory 1503 can include one or more memory devices, such as read-only memory (ROM) 1504 and a random-access memory (RAM) 1505. The ROM 1504 is coupled to the system bus 1502 and may include a basic input / output system (BIOS), which controls certain basic functions of the computer system 1500. The RAM is read-write memory coupled to the system bus 1502 for use by the processors 1501. The system memory 1503 provides temporary memory space for operations of said instructions during operation. The system memory 1503 can include random access memory (RAM), read-only memory, flash memory, or any other suitable memory systems.

[0090] The computer system 1500 comprises an input / output (I / O) adapter 1506 and a communications adapter 1507 coupled to the system bus 1502. The I / O adapter 1506 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 1508 and / or any other similar component. The I / O adapter 1506 and the hard disk 1508 are collectively referred to herein as a mass storage 1510.

[0091] Software 1511 for execution on the computer system 1500 may be stored in the mass storage 1510. The mass storage 1510 is an example of a tangible storage medium readable by the processors 1501, where the software 1511 is stored as instructions for execution by the processors 1501 to cause the computer system 1500 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 1507 interconnects the system bus 1502 with a network 1512, which may be an outside network, enabling the computer system 1500 to communicate with other such systems. In one aspect, a portion of the system memory 1503 and the mass storage 1510 collectively store an operating system, which may be any appropriate operating system to coordinate the functions of the various components shown in FIG. 15.

[0092] Additional input / output devices are shown as connected to the system bus 1502 via a display adapter 1515 and an interface adapter 1516 and. In one aspect, the adapters 1506, 1507, 1515, and 1516 may be connected to one or more I / O buses that are connected to the system bus 1502 via an intermediate bus bridge (not shown). A display 1519 (e.g., a screen or a display monitor) is connected to the system bus 1502 by a display adapter 1515, which may include a graphics controller to improve the performance of graphicsintensive 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 1502 via the interface adapter 1516, 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. 15, the computer system 1500 includes processing capability in the form of the processors 1501, and storage capability including the system memory 1503 and the mass storage 1510, input means such as the buttons, touchscreen, and output capability including the speaker 1523 and the display 1519.

[0093] In some aspects, the communications adapter 1507 can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network 1512 may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An externalcomputing device may connect to the computer system 1500 through the network 1512.In some examples, an external computing device may be an external web server or a cloud computing node.

[0094] It is to be understood that the block diagram of FIG. 15 is not intended to indicate that the computer system 1500 is to include all of the components shown in FIG. 15. Rather, the computer system 1500 can include any appropriate fewer or additional components not illustrated in FIG. 15 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the aspects described herein with respect to computer system 1500 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.

[0095] In some aspects, a computer-implemented method for surgical standardization metric generation for surgical workflow variation can include displaying a user interface that provides user configurability of a principal workflow for a surgical procedure, accessing a set of surgical workflows comprising a plurality of phases in sequences of previous performances of the surgical procedure, determining a distance-based metric that summarizes variability of the set of surgical workflows using one or more standardization customizations, and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0096] In some aspects, the computer-implemented method can include grouping the set of surgical workflows occurring over a period of time as a plurality of subsets, determining the distance-based metric that summarizes variability of the subsets, and outputting a visualization of variability of the subsets, wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences, and the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

[0097] In some aspects, the one or more standardization customizations change a weighting of distance values in determining the distance-based metric, and the one or more standardization customizations comprise one or more user-selectable adjustments to the weighting of distance values based on one or more of: phase toggling, phase repetition, optional phases, patient specific phases, phase duration, and rare transitions.

[0098] In some aspects, a custom workflow creation user interface can be displayed, and a definition of the principal workflow can be changed based on input received through the custom workflow creation user interface.

[0099] In some aspects, a computer program product includes a memory device with computer readable instructions stored thereon, wherein executing the computer readable instructions by one or more processing units causes the one or more processing units to perform a plurality of operations. The operations can include displaying a user interface that provides user configurability of a principal workflow for a surgical procedure, accessing a set of surgical workflows comprising a plurality previous performances of the surgical procedure, determining a distance-based metric that summarizes variability of the set of surgical workflows, and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0100] In some aspects, the operations can include receiving one or more standardization customizations for determining the distance-based metric, and changing a weighting of distance values in determining the distance-based metric based on the one or more standardization customizations.

[0101] In some aspects, the operations can include other aspects as previously described.

[0102] The present invention may be a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product may include a computer-readable storage medium (or media) having computer- readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0103] The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, amagnetic 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.

[0104] 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.

[0105] Computer-readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either sourcecode or object code written in any combination of one or more programming languages, including an object-oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer-readable program instructions may executeentirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some aspects, electronic circuitry including, for example, programmable logic circuitry, field- programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer-readable program instruction by utilizing state information of the computer- readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0106] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to aspects of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer- readable program instructions.

[0107] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0108] 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 instructionswhich 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.

[0109] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0110] The descriptions of the various aspects of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the aspects disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described aspects. The terminology used herein was chosen to best explain the principles of the aspects, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the aspects described herein.

[0111] Various aspects of the invention are described herein with reference to the related drawings. Alternative aspects of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and / or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirectpositional 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.

[0112] 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.

[0113] Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. The terms “at least one” and “one or more” may be understood to include any integer number greater than or equal to one, i.e., one, two, three, four, etc. The terms “a plurality” may be understood to include any integer number greater than or equal to two, i.e., two, three, four, five, etc. The term “connection” may include both an indirect “connection” and a direct “connection.”

[0114] 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.

[0115] For the sake of brevity, conventional techniques related to making and using aspects of the invention may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and / or process details.

[0116] 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 theexample, 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.

[0117] 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).

[0118] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0119] The following examples are illustrative of the techniques described herein.

[0120] Example 1. A system comprising: a memory device; and one or more processors coupled with the memory device, the one or more processors configured to: display a user interface that provides user configurability of a principal workflow and one or more standardization customizations for a surgical procedure; access a set of surgical workflows comprising a plurality of phases in sequences of previous performances of the surgical procedure; determine a distance-based metric that summarizes variability of the set of surgical workflows using the one or more standardization customizations; and display a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0121] Example 2. The system of Example 1, wherein the phases are determined based on video of multiple surgical cases of the surgical procedure.

[0122] Example 3. The system of Example 1, wherein the one or more processors are configured to: determine variability between one or more of: multiple surgeons in a same department, multiple surgeons between departments, and a same surgeon performing multiple surgeries.

[0123] Example 4. The system of Example 1, wherein the one or more processors are configured to: group the set of surgical workflows occurring over a period of time as a plurality of subsets; determine the distance-based metric that summarizes variability of the subsets; and output a visualization of variability of the subsets.

[0124] Example 5. The system of Example 4, wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences.

[0125] Example 6. The system of Example 5, wherein the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

[0126] Example 7. The system of Example 1, wherein the distance-based metric is determined using pairwise distances for the set of surgical workflows and summarized by aggregating the pairwise distances into a single ensemble measure or a multi-dimensional distance measure.

[0127] Example 8. The system of Example 1, wherein the principal workflow is a target workflow, and the distance-based metric is determined using distances for the set of surgical workflows to the target workflow and summarized by aggregating the distances into a single ensemble measure or a multi-dimensional distance measure.

[0128] Example 9. The system of Example 1, wherein the one or more standardization customizations change a weighting of distance values in determining the distance-based metric.

[0129] Example 10. The system of Example 9, wherein the one or more standardization customizations comprise one or more user-selectable adjustments to the weighting of distance values based on one or more of: phase toggling, phase repetition, optional phases, patient specific phases, phase duration, and rare transitions.

[0130] Example 11. The system of Example 1, wherein the distance-based metric is a modified edit distance that is adjusted based on one or more of: phase repetition in a same surgical workflow, optional phases identified in the phases, surgical phase duration relative to a typical phase duration, duration returning to previously performed phase in the same surgical workflow, occurrence of a predetermined subsequence of the phases, phase occurrence relative to a beginning and end of the surgical procedure, phase transition likelihood, use of discretization of the phases, and deviation duration from the principal workflow.

[0131] Example 12. The system of Example 1, wherein the one or more processors are configured to: display a custom workflow creation user interface; and change a definition of the principal workflow based on input received through the custom workflow creation user interface.

[0132] Example 13. A computer-implemented method for surgical standardization metric generation for surgical workflow variation, the method comprising: displaying a user interface that provides user configurability of a principal workflow for a surgical procedure; accessing a set of surgical workflows comprising a plurality of phases in sequences of previous performances of the surgical procedure; determining a distancebased metric that summarizes variability of the set of surgical workflows using one or more standardization customizations; and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0133] Example 14. The computer-implemented method of Example 13, further comprising: grouping the set of surgical workflows occurring over a period of time as a plurality of subsets; determining the distance-based metric that summarizes variability of the subsets; and outputting a visualization of variability of the subsets, wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences, and the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

[0134] Example 15. The computer-implemented method of Example 13, wherein the one or more standardization customizations change a weighting of distance values indetermining the distance-based metric, and the one or more standardization customizations comprise one or more user-selectable adjustments to the weighting of distance values based on one or more of: phase toggling, phase repetition, optional phases, patient specific phases, phase duration, and rare transitions.

[0135] Example 16. The computer-implemented method of Example 13, further comprising: displaying a custom workflow creation user interface; and changing a definition of the principal workflow based on input received through the custom workflow creation user interface.

[0136] Example 17. A computer program product comprising a memory device with computer readable instructions stored thereon, wherein executing the computer readable instructions by one or more processing units causes the one or more processing units to perform a plurality of operations comprising: displaying a user interface that provides user configurability of a principal workflow for a surgical procedure; accessing a set of surgical workflows comprising a plurality previous performances of the surgical procedure; determining a distance-based metric that summarizes variability of the set of surgical workflows; and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

[0137] Example 18. The computer program product of Example 17, wherein the operations further comprise: receiving one or more standardization customizations for determining the distance-based metric; and changing a weighting of distance values in determining the distance-based metric based on the one or more standardization customizations.

[0138] Example 19. The computer program product of Example 17, wherein the operations further comprise: grouping the set of surgical workflows occurring over a period of time as a plurality of subsets; determining the distance-based metric that summarizes variability of the subsets; and outputting a visualization of variability of the subsets, wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences, and the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

[0139] Example 20. The computer program product of Example 17, wherein the operations further comprise: displaying a custom workflow creation user interface; and changing a definition of the principal workflow based on input received through the custom workflow creation user interface.

Claims

CLAIMSWhat is claimed is:

1. A system (100, 200, 1500) comprising: a memory device; and one or more processors coupled with the memory device, the one or more processors configured to: display a user interface (400) that provides user configurability of a principal workflow and one or more standardization customizations for a surgical procedure; access a set of surgical workflows comprising a plurality of phases in sequences of previous performances of the surgical procedure; determine a distance-based metric that summarizes variability of the set of surgical workflows using the one or more standardization customizations; and display a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

2. The system (100, 200, 1500) of claim 1, wherein the phases are determined based on video of multiple surgical cases of the surgical procedure.

3. The system (100, 200, 1500) of claims 1 or 2, wherein the one or more processors are configured to: determine variability between one or more of: multiple surgeons in a same department, multiple surgeons between departments, and a same surgeon performing multiple surgeries.

4. The system (100, 200, 1500) of any of claims 1 to 3, wherein the one or more processors are configured to: group the set of surgical workflows occurring over a period of time as a plurality of subsets;determine the distance-based metric that summarizes variability of the subsets; and output a visualization of variability of the subsets, optionally wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences, and optionally wherein the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

5. The system (100, 200, 1500) of any of claims 1 to 4, wherein the distance-based metric is determined using pairwise distances for the set of surgical workflows and summarized by aggregating the pairwise distances into a single ensemble measure or a multi-dimensional distance measure.

6. The system (100, 200, 1500) of any of claims 1 to 5, wherein the principal workflow is a target workflow, and the distance-based metric is determined using distances for the set of surgical workflows to the target workflow and summarized by aggregating the distances into a single ensemble measure or a multi-dimensional distance measure.

7. The system (100, 200, 1500) of any of claims 1 to 6, wherein the one or more standardization customizations change a weighting of distance values in determining the distance-based metric, and optionally wherein the one or more standardization customizations comprise one or more user-selectable adjustments to the weighting of distance values based on one or more of: phase toggling, phase repetition, optional phases, patient specific phases, phase duration, and rare transitions.

8. The system (100, 200, 1500) of any of claims 1 to 7, wherein the distance-based metric is a modified edit distance that is adjusted based on one or more of: phase repetition in a same surgical workflow, optional phases identified in the phases, surgical phase duration relative to a typical phase duration, duration returning to previously performed phase in the same surgical workflow, occurrence of a predetermined subsequence of the phases, phase occurrence relative to a beginning and end of the surgical procedure, phase transition likelihood, use of discretization of the phases, and deviation duration from the principal workflow.

9. The system (100, 200, 1500) of any of claims 1 to 8, wherein the one or more processors are configured to: display a custom workflow creation user interface (500); and change a definition of the principal workflow based on input received through the custom workflow creation user interface (500).

10. A computer-implemented method for surgical standardization metric generation for surgical workflow variation, the method comprising: displaying a user interface (400) that provides user configurability of a principal workflow for a surgical procedure; accessing a set of surgical workflows comprising a plurality of phases in sequences of previous performances of the surgical procedure; determining a distance-based metric that summarizes variability of the set of surgical workflows using one or more standardization customizations; and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

11. The computer-implemented method of claim 10, further comprising: grouping the set of surgical workflows occurring over a period of time as a plurality of subsets; determining the distance-based metric that summarizes variability of the subsets; and outputting a visualization of variability of the subsets, wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences, and the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

12. The computer-implemented method of claims 10 or 11, wherein the one or more standardization customizations change a weighting of distance values in determining the distance-based metric, and the one or more standardization customizations comprise one or more user-selectable adjustments to the weighting of distance values based on one or more of: phase toggling, phase repetition, optional phases, patient specific phases, phase duration, and rare transitions.

13. The computer-implemented method of any of claims 10 to 12, further comprising: displaying a custom workflow creation user interface (500); and changing a definition of the principal workflow based on input received through the custom workflow creation user interface (500).

14. A computer program product comprising a memory device with computer readable instructions stored thereon, wherein executing the computer readable instructions by one or more processing units causes the one or more processing units to perform a plurality of operations comprising: displaying a user interface (400) that provides user configurability of a principal workflow for a surgical procedure; accessing a set of surgical workflows comprising a plurality previous performances of the surgical procedure; determining a distance-based metric that summarizes variability of the set of surgical workflows; and displaying a summary of surgical performance results comprising an indication of variability with respect to the principal workflow based on the distance-based metric.

15. The computer program product of claim 14, wherein the operations further comprise: receiving one or more standardization customizations for determining the distancebased metric;changing a weighting of distance values in determining the distance-based metric based on the one or more standardization customizations; grouping the set of surgical workflows occurring over a period of time as a plurality of subsets; determining the distance-based metric that summarizes variability of the subsets; and outputting a visualization of variability of the subsets, wherein the visualization of variability of the subsets comprises a scatter plot of variability of the subsets with a trend line and one or more flow diagrams of surgical workflow sequences, and the one or more flow diagrams of surgical workflow sequences comprise a most common surgical workflow and a surgical workflow that was least similar to the most common surgical workflow.

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