Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
  • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
  • G06V20/44—Event detection
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
  • G06V20/49—Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes

Definitions

• the present invention relates in general to computing technology and relates more particularly to computing technology for aligned workflow compression and multidimensional workflow alignment.
• Computer-assisted systems particularly computer-assisted surgery systems (CASs)
• CASs computer-assisted surgery systems
• video data can be stored and/or streamed.
• the video data can be used to augment a person’s physical sensing, perception, and reaction capabilities.
• 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.
• 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.
• a system includes a machine learning processing system that includes one or more machine learning models that are trained to identify a plurality of phases in a video of a surgical procedure.
• the system also includes a data analysis system configured to quantify variation in surgical approaches in a plurality of videos capturing a same type of surgical procedure.
• the quantifying variation in surgical approaches includes receiving a plurality of surgical videos, each of the plurality of surgical videos capturing a workflow of the same type of surgical procedure.
• Each of the plurality of surgical videos is segmented into a segmented workflow that includes a plurality of phases, the segmenting is based on phases identified by the machine learning processing system.
• the phases in the segmented workflows are aligned to an average workflow of the segmented workflows to create a plurality of aligned workflows.
• Each of the plurality of aligned workflows has a same number of phases as the average workflow.
• An entropy is calculated for each phase in the aligned workflows based on a Chao-Shen estimator.
• the entropy for each phase in the aligned workflow is output to a display device for display, where the entropy represents variation in surgical approaches to the surgical procedure.
• calculating the entropy includes scaling the entropy by its maximum possible value to a value that falls in a specified range of values.
• compact multiple alignment is used to align each sequence within the set to the corresponding average sequence. This alignment is carried out on each sequence using dynamic time warping (DTW) with respect to the average. CMA allows multiple phases of the sequence to be aligned to a single phase in the average. Once all the sequences are aligned, they are “unpacked,” which includes expanding, or stretching, each sequence so that each phase is only aligned to a single phase. The alignment of the sequences is maintained by inserting repeated values of the previous phase in sequences with no phases to unpack. This prevents any phases from being ignored within sequences due to the alignment, therefore preventing any data loss.
• DTW dynamic time warping
• Entropy gives a numerical value of the variation across the set at each point in the surgery based purely on sequencing rather than timing.
• Contemporary approaches use one of several methods to calculate Shannon’s entropy, which is based on the probability of each phase occurring. Previous studies have found that for small sample sizes Shannon’s entropy can be biased.
• One or more aspects of the present invention address the bias issues associated with small sample sizes by using the Chao-Shen estimator of Shannon’s entropy which has been found to be efficient for a range of sample sizes.
• the entropy calculations are scaled by the maximum possible entropy, given the number of sequences and number of possible phases, so that the entropy values are from 0 to 1. This allows the entropy to be directly comparable between groups.
• phases are expanded in all workflows to the maximum length required. For example, if in one workflow there are four phases aligned to one phase in the average workflow, all of the workflows will be stretched to a length of four at this point. This is done to ensure that no information is lost (e.g., no phases are deleted) and that all workflows are the same length at the end of the process.
• This approach, of stretching the workflows can result in the aligned workflows being longer than the average workflow, thus making it appear that some phases are repeated multiple times in a row.
• the method can also return how often each phase occurred in the aligned workflows to show what the most common phases were at this point.
• Aligned workflow compression can make it easier to understand the output that describes observed variation in surgical procedures because the visualization shows one entropy value for each phase in the average workflow.
• workflows contain one-dimensional data, such as a sequence of surgical phases.
• workflows contain multidimensional data which includes two or more data values (e.g., surgical phase, surgical instrument, and anatomy) for each portion of a surgery.
• data values e.g., surgical phase, surgical instrument, and anatomy
• One or more aspects described herein can be utilized to determine and to output visualizations of variations in multidimensional data related to a group of surgical procedures. For example, variations in multi-dimensional data (e.g., features such as surgical phase, surgical instrument, kinematics, anatomy, etc.) can be tracked at each point in a surgery.
• the averages are calculated in the same way that they are calculated for one-dimensional data, but at each point in the sequence the mode value is found in each dimension of the data.
• each workflow is aligned to the average it is aligned based on multiple data dimensions. For example, if two-dimensional data is being analyzed, the values at each point in the surgery can be represented as (value of dimension one, value of dimension two). The distance between (A, B) and (A, C) is equal to 0.5 (i.e., one of the two dimensions have different values). Similarly, the distance between (C, B) and (A, C) is equal to 1 (i.e., both dimensions have different values).
• one or more aspects of the present invention can be utilized to analyze Laparoscopic Roux-en-Y Gastric Bypass surgeries.
• the averaging method can be used to generate an average workflow for each set of surgeries (e.g., one set from each of several different hospitals). This can be used to identify differences in approaches for Laparoscopic Roux-en-Y Gastric Bypass between the hospitals.
• Comparison of the average workflows can identify, for example, one hospital of the group using a retro colic approach as standard and the most common approach used at each of the other hospitals. This provides an easily understandable way of comparing the approaches for each hospital.
• the average workflow also identifies surgical tools and each different combination of a surgical phase and a surgical tool represents a different point in the sequence in the average workflow.
• one or more aspects of the present invention are applied to Laparoscopic Cholecystectomy surgeries and the average workflows may be found based on the assigned grade of the gallbladder, with higher grades resulted in more complex workflows with repetitions of phases showing the increase in complexity of the surgery.
• the average workflows described herein can allow for easy comparison between groups as well as identification of their key characteristics.
• one or more aspects of the present invention provide multiple alignment of workflows which allows for the possibility of many comparative measures to be calculated.
• the compact multiple alignment of the surgical workflows based on the average generates a set of aligned sequences of the same length with clear points of similarity and difference.
• the use of entropy can provide a quantifiable value of variation at each point in the surgery. Due to the scaling of the entropy the calculated values are directly comparable between groups. This can allow the identification of high variation within the surgery and the comparison of variation between hospitals.
• Measurable improvement in surgical interventions can be achieved by objectively quantifying surgical standardization, process efficiency and patient outcomes.
• One or more aspects of the present invention provide a methodology that can effectively achieve this for categorical data, such as surgical phase data, surgical tool data, and/or patient anatomy data.
• One or more aspects of the present invention improve upon contemporary approaches that use NLTS on surgical phase data by the use of a new, modified ADBA method and the use of the Chao- Shen estimator of Shannon’s entropy scaled by its maximum possible value.
• One or more aspects of the present invention provide a representative average sequence for a data set of surgical workflows, which can easily be compared to other groups (or data sets), and a quantifiable value of variation throughout surgery which can be used to understand surgical standardization.
• a robust standardization metric such as that provided by one or more aspects of the present invention described herein allows a surgical approach to be measured and improvements due to training to be tracked and quantified.
• surgical data that is captured by a computer-assisted surgical (CAS) system and segmented into surgical phases is input to the analysis described herein to quantify variation is surgical approaches to a medical procedure.
• CAS computer-assisted surgical
• the CAS system 100 includes at least a computing system 102, a video recording system 104, and a surgical instrumentation system 106.
• an actor 112 can be medical personnel that uses the CAS system 100 to perform a surgical procedure on a patient 110. Medical personnel can be a surgeon, assistant, nurse, administrator, or any other actor that interacts with the CAS system 100 in a surgical environment.
• the surgical procedure can be any type of surgery, such as but not limited to cataract surgery, laparoscopic cholecystectomy, endoscopic endonasal transsphenoidal approach (eTSA) to resection of pituitary adenomas, or any other surgical procedure.
• actor 112 can be a technician, an administrator, an engineer, or any other such personnel that interacts with the CAS system 100.
• 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, etc.
• 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).
• certain surgical instruments 108 e.g., forceps
• a particular anatomical structure of the patient may be the target of the surgical action(s).
• the video recording system 104 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, etc.
• 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.
• 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 1700 of FIG. 17.
• 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.
• the computing system 102 includes one or more trained machine learning models that can detect and/or predict features of/from the surgical procedure that is being performed or has been performed earlier.
• Features can include structures such as anatomical structures, surgical instruments 108 in the captured video of the surgical procedure.
• Features can further include events such as phases, actions in the surgical procedure.
• Features that are detected can further include the actor 112 and/or patient 110.
• the computing system 102 in one or more examples, can provide recommendations for subsequent actions to be taken by the actor 112.
• the computing system 102 can provide one or more reports based on the detections.
• the detections by the machine learning models can be performed in an autonomous or semi-autonomous manner.
• the machine learning models can include artificial neural networks, such as deep neural networks, convolutional neural networks, recurrent neural networks, encoders, decoders, or any other type of machine learning model.
• the machine learning models can be trained in a supervised, unsupervised, or hybrid manner.
• the machine learning models can be trained to perform detection and/or prediction using one or more types of data acquired by the CAS system 100.
• the machine learning models can use the video data captured via the video recording system 104.
• the machine learning models use the surgical instrumentation data from the surgical instrumentation system 106.
• the machine learning models use a combination of video data and surgical instrumentation data.
• the machine learning models can also use audio data captured during the surgical procedure.
• the audio data can include sounds emitted by the surgical instrumentation system 106 while activating one or more surgical instruments 108.
• the audio data can include voice commands, snippets, or dialog from one or more actors 112.
• the audio data can further include sounds made by the surgical instruments 108 during their use.
• the machine learning models can detect surgical actions, surgical phases, anatomical structures, surgical instruments, and various other features from the data associated with a surgical procedure. The detection can be performed in real-time in some examples.
• the computing system 102 analyzes the surgical data, i.e., the various types of data captured during the surgical procedure, in an offline manner (e.g., post-surgery).
• the machine learning models detect surgical phases based on detecting some of the features such as the anatomical structure, surgical instruments, etc.
• the data collection system 150 can be part of the video recording system 104, or vice-versa.
• 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, etc.), data manipulation commands (e.g., browse, copy, paste, move, delete, create, compress, etc.), data manipulation results, etc.
• the video captured by the video recording system 104 is stored on the data collection system 150.
• the computing system 102 curates parts of the video data being stored on the data collection system 150.
• the computing system 102 filters the video captured by the video recording system 104 before it is stored on the data collection system 150.
• the computing system 102 filters the video captured by the video recording system 104 after it is stored on the data collection system 150.
• FIG. 2 a surgical procedure system 200 is generally shown in accordance with one or more aspects.
• the example of FIG. 2 depicts a surgical procedure support system 202 that can include or may be coupled to the CAS system 100 of FIG. 1.
• the surgical procedure support system 202 can acquire image or video data using one or more cameras 204.
• the surgical procedure support system 202 can also interface with a plurality of sensors 206 and 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.
• FIG. 3 a system 300 for analyzing video and data is generally shown according to one or more 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 300 uses data streams in the surgical data to identify procedural states according to some aspects.
• 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.
• 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.
• 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.
• 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.
• the 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.
• 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.
• devices that “train” the models are separate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained machine learning models 330.
• Machine learning processing system 310 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.
• the other data can include imagesegmentation 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.
• 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.
• the machine learning training system 325 uses the recorded data in the data store 320, which can include the simulated surgical data (e.g., set of virtual images) and actual 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 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.
• 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.
• the machine learning models 330 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.
• 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.
• the coordinates can provide a bounding box.
• the coordinates can provide boundaries that surround the structure(s) being predicted.
• the machine learning models 630 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.
• 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.
• a phase relates to a biological state of a patient undergoing a surgical procedure.
• the biological state can indicate a complication (e.g., blood clots, clogged arteries/veins, etc.), pre-condition (e.g., lesions, polyps, etc.).
• the machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, etc.
• 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.
• 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.
• 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 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.).
• 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.
• 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).
• 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.
• the cameras can be mounted on surgical instruments, walls, or other locations in the operating room.
• FIG. 4 a block diagram of a data analysis system 400 for quantifying variations in surgical approaches is generally shown in accordance with one or more aspects. All or a portion of data analysis system 400 can be implemented by CAS system 100 of FIG. 1, and/or by computer system 1700 of FIG. 17.
• the data analysis system 400 shown in FIG. 4 includes surgical workflow database 402, surgical workflow approach analysis module 404, surgical variation visualization module 406, surgical approach variation quantification module 408, and display device 410. Examples of the processing that can be performed by the modules shown in FIG. 4 are shown FIGs. 5-16 and described below.
• the average is updated by finding the barycenter average of each element based on the sequences in the set. However, this is not possible with categoric data. Therefore, in one or more aspects of the present invention, the mode value is found for each element. In the case of multiple modes different averages are created, one with each mode as the element value. The best average, the medoid, is then found and used going forward.
• the averages combining each of the potential modes include:
• the DTW of the average to each sequence in the set is computed.
• the average is used to calculate the scaling coefficients.
• the scaling coefficients are a value for each element in the average which represents whether on average the element was matched to multiple elements in the sequences or vice versa. This gives an indication of whether the average is too long or too short at this point in comparison to the sequences.
• the alignment coefficient is calculated for each element in the average; 0 if the element is matched to one element in the sequence, if it is matched to multiple elements in the sequence it is given a value of the number of elements it is matched to -1, or 1 - the number of elements in the average matched to one element in the sequence.
• the scaling coefficients are then the sum of the alignment coefficients for each element for the average when aligned to each sequence in the set.
• FIG. 8 which depicts an alignment of the following example, for a sequences ABA, AAABA and A and an average sequence of ABA.
• ABA aligned to the first sequence would result in alignment coefficients 0, 0 and 0 as each element in the average would be matched to one in the sequence.
• A the first element of the average
• the following elements would be matched to one element in the sequence giving alignment coefficients of 2, 0 and 0.
• DBA such as DBA method 600 of FIG. 6, is run on the partition average to optimize the values. This involves optimizing the values of the partition average based on the mode values of the corresponding partitions of the sequences. Processing continues at block 714 with finding the new scaling factors as explained previously. If the sign of the scaling factors has changed the processing continues at block 716. Otherwise, if the sign of the scaling factors has not changed, the average is stored, and processing returns to block 708 or 710. At block 716, the stored average which is the medoid is used as the new average for the partition. Once the processing in block 730 is repeated for every partition, processing continues at block 718 with concatenating the partition averages together to generate the new average sequence and processing continues at block 720.
• an average sequence is chosen from a list by selecting the medoid one (the optimization sections in DBA and ADBA and at the end of the algorithm).
• the sequence with the lowest Levenshtein distance when compared to the data set is used. This is a common distance measure when comparing two lists of letters, as our phase data is.
• the processing shown in FIG.7 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. 7 are to be included in every case. Additionally, the processing shown in FIG. 7 can include any suitable number of additional operations.
• entropy is calculated for each phase, or segment, in the aligned workflows to determine the entropy, or variation, across the data at each point in the workflow.
• entropy is calculated for each phase, or segment, in the aligned workflows to determine the entropy, or variation, across the data at each point in the workflow.
• phases that have been stretched into multiple phases there will be an entropy value for each phase in the stretched phase, and multiple phases in the resulting aligned workflows may correspond to a single phase in the average workflow. This can be confusing for some users as some phases are stretched out, and may also result in potentially misleading entropy values during these stretched phases.
• FIG. 10 a block diagram 1000 of a compression and entropy calculation of aligned workflows, and a visualization of the results are generally shown in accordance with one or more aspects.
• each of the phases in the aligned workflows 904 of FIG. 9 are aligned to one phase in the average workflow 908.
• the first two phases of the aligned workflows 904 are associated with the first phase in the average workflow 908.
• FIG. 11 also depicts the compressed aligned workflows 1004 of FIG. 10 along with their corresponding entropy values 1002 in graph form.
• the aligned workflows 904 have been compressed so that the number of phases in each of the aligned workflows 1004 is the same as the number of phases in the average workflow 908.
• the entropy value 1002, in graph form that is calculated and displayed for each of phases in the average workflow 908.
• the visualization of the compressed aligned workflow 1004 depicts one column and one entropy value 1002 for each phase in the average workflow 908. In this manner, how often each phase occurs at each point of the surgery is shown in an easy to understand format.
• the sequence of the phases shown in the two- dimensional workflow 1204 include (Phase A, Tool A), (Phase A, Tool B), (Phase A, Tool C), (Phase A, Tool B), (Phase B, Tool B), (Phase B, Tool C), (Phase B, Tool A), (Phase C, Tool A), (Phase C, Tool C), and (Phase C, Tool None).
• FIG. 13 a block diagram 1300 of multi-dimensional workflow alignment is generally shown in accordance with one or more aspects.
• the block diagram 1300 includes multi-dimensional workflows 1302, an average multi-dimensional workflow 1306, and aligned multi-dimensional workflows 1304.
• the aligned multi-dimensional workflows 1304 can be used to calculate the surgical variation in the same way as previously described.
• the techniques for onedimensional data can be extended into multiple dimensions so that an average is calculated based on multiple dimensions, alignment is carried out, and entropy is calculated.
• the counts of how often each tool occurs at each point is also used.
• the distance is found between them to give a cost value of each potential alignment. For example, in the example shown in FIG. 13, the cost increases by .5 if the surgical phases are different and the cost increases by .5 if the surgical tools are different.
• the maximum possible value is calculated based on the possible values of all surgical phases and tools. For example, for one dimensional data, the distance between a first sequence A, B, C and a second sequence A, D, C is 1 as there is one phase that is different. For multidimensional data, the distance between a sequence (A,D), (B,D), (C,D) and (A,D), (D,D), (C,D) is .5. The average and alignment can be calculated using this variation in the distance scores between workflows.
• the average multi-dimensional workflow 1306 is mostly the mode surgical phase and mode surgical tool at each point, or segment. Some differences are due to the alignment as the multi-dimensional workflows 1302 are of different lengths.
• the surgical phase and tool variation are calculated separately to give users a view on how they vary in their surgical phase workflow and on how they vary in their tooling workflow.
• FIG. 14 an example of a visualization 1400 of workflow standardization is generally shown in accordance with one or more aspects.
• the visualization 1400 shown in FIG. 14 shows the average entropy of a surgery each month with the shaded sections corresponding to the error bands of the calculation. This shows workflow variation over time as well as the potential error of the entropy calculation based on the number of surgeries carried out each month.
• FIG. 15 an example of a visualization 1500 of variation over surgery is generally shown in accordance with one or more aspects. This shows the entropy throughout a surgery from the start point 0 on the left to the end of the surgery at point 1 on the right. The entropy throughout the surgery is shown for a particular surgeon in blue compared to the relevant department in pink. This allows users to compare their variation with that of their department to determine whether their variation is in line with their peers.
• the visualizations 1400 1500 1600 shown in FIG. 14, FIG. 15, and FIG. 16 are examples of the type of data that may be generated and displayed to a user.
• a graphical representation of the identified surgical approaches is output for display on a display device.
• User input may be received via a user interface of the graphical representation and a second graphical representation including for example, providers that use one or more of the identified surgical approaches, may be output for display on the display device.
• the visualization is interactive and can be modified based on input from a user.
• any number of different visualizations containing different data and in different formats can be provided by the surgical variation visualization module 406 of FIG. 4 based on contents of the surgical workflow data for output to display device 410 of FIG. 4.
• the computer system 1700 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 1700 can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others.
• the computer system 1700 may be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone.
• computer system 1700 may be a cloud computing node.
• Computer system 1700 may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer system.
• 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 1700 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network.
• program modules may be located in both local and remote computer system storage media, including memory storage devices.
• the ROM 1704 is coupled to the system bus 1702 and may include a basic input/output system (BIOS), which controls certain basic functions of the computer system 1700.
• BIOS basic input/output system
• the RAM is read-write memory coupled to the system bus 1702 for use by the processors 1701.
• the system memory 1703 provides temporary memory space for operations of said instructions during operation.
• the system memory 1703 can include random access memory (RAM), read-only memory, flash memory, or any other suitable memory systems.
• the computer system 1700 comprises an input/output (I/O) adapter 1706 and a communications adapter 1707 coupled to the system bus 1702.
• the I/O adapter 1706 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 1708 and/or any other similar component.
• SCSI small computer system interface
• the I/O adapter 1706 and the hard disk 1708 are collectively referred to herein as a mass storage 1710.
• Additional input/output devices are shown as connected to the system bus 1702 via a display adapter 1715 and an interface adapter 1716 and.
• the adapters 1706, 1707, 1715, and 1716 may be connected to one or more I/O buses that are connected to the system bus 1702 via an intermediate bus bridge (not shown).
• a display 1719 e.g., a screen or a display monitor
• a display adapter 1715 which may include a graphics controller to improve the performance of graphics-intensive applications and a video controller.
• a keyboard, a mouse, a touchscreen, one or more buttons, a speaker, etc. can be interconnected to the system bus 1702 via the interface adapter 1716, 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).
• PCI Peripheral Component Interconnect
• the computer system 1700 includes processing capability in the form of the processors 1701, and storage capability including the system memory 1703 and the mass storage 1710, input means such as the buttons, touchscreen, and output capability including the speaker 1723 and the display 1719.
• the communications adapter 1707 can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others.
• the network 1712 may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others.
• An external computing device may connect to the computer system 1700 through the network 1712.
• an external computing device may be an external web server or a cloud computing node.
• FIG. 17 is not intended to indicate that the computer system 1700 is to include all of the components shown in FIG. 17. Rather, the computer system 1700 can include any appropriate fewer or additional components not illustrated in FIG. 17 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the aspects described herein with respect to computer system 1700 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.
• 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 e.g., an application, among others
• 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
• 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).
• 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.
• DSPs digital signal processors
• ASICs application-specific integrated circuits
• FPGAs field programmable logic arrays
• processors 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.

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• 2023
  • 2023-09-07 CN CN202380062596.5A patent/CN119790440A/zh active Pending
  • 2023-09-07 EP EP23768255.4A patent/EP4584758A1/de active Pending
  • 2023-09-07 WO PCT/EP2023/074575 patent/WO2024052458A1/en not_active Ceased

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