Using hand pose estimation to automate open surgery training feedback.

PURPOSE: This research aims to facilitate the use of state-of-the-art computer vision algorithms for the automated training of surgeons and the analysis of surgical footage. By estimating 2D hand poses, we model the movement of the practitioner's hands, and their interaction with surgical instruments, to study their potential benefit for surgical training. METHODS: We leverage pre-trained models on a publicly available hands dataset to create our own in-house dataset of 100 open surgery simulation videos with 2D hand poses. We also assess the ability of pose estimations to segment surgical videos into gestures and tool-usage segments and compare them to kinematic sensors and I3D features. Furthermore, we introduce 6 novel surgical dexterity proxies stemming from domain experts' training advice, all of which our framework can automatically detect given raw video footage. RESULTS: State-of-the-art gesture segmentation accuracy of 88.35% on the open surgery simulation dataset is achieved with the fusion of 2D poses and I3D features from multiple angles. The introduced surgical skill proxies presented significant differences for novices compared to experts and produced actionable feedback for improvement. CONCLUSION: This research demonstrates the benefit of pose estimations for open surgery by analyzing their effectiveness in gesture segmentation and skill assessment. Gesture segmentation using pose estimations achieved comparable results to physical sensors while being remote and markerless. Surgical dexterity proxies that rely on pose estimation proved they can be used to work toward automated training feedback. We hope our findings encourage additional collaboration on novel skill proxies to make surgical training more efficient.

Open surgery tool classification and hand utilization using a multi-camera system.

PURPOSE: The goal of this work is to use multi-camera video to classify open surgery tools as well as identify which tool is held in each hand. Multi-camera systems help prevent occlusions in open surgery video data. Furthermore, combining multiple views such as a top-view camera covering the full operative field and a close-up camera focusing on hand motion and anatomy may provide a more comprehensive view of the surgical workflow. However, multi-camera data fusion poses a new challenge: A tool may be visible in one camera and not the other. Thus, we defined the global ground truth as the tools being used regardless their visibility. Therefore, tools that are out of the image should be remembered for extensive periods of time while the system responds quickly to changes visible in the video. METHODS: Participants (n = 48) performed a simulated open bowel repair. A top-view and a close-up cameras were used. YOLOv5 was used for tool and hand detection. A high-frequency LSTM with a 1-second window at 30 frames per second (fps) and a low-frequency LSTM with a 40-second window at 3 fps were used for spatial, temporal, and multi-camera integration. RESULTS: The accuracy and F1 of the six systems were: top-view (0.88/0.88), close-up (0.81,0.83), both cameras (0.9/0.9), high-fps LSTM (0.92/0.93), low-fps LSTM (0.9/0.91), and our final architecture the multi-camera classifier(0.93/0.94). CONCLUSION: Since each camera in a multi-camera system may have a partial view of the procedure, we defined a 'global ground truth.' Defining this at the data labeling phase emphasized this requirement at the learning phase, eliminating the need for any heuristic decisions. By combining a system with a high fps and a low fps from the multiple camera array, we improved the classification abilities of the global ground truth.

Using open surgery simulation kinematic data for tool and gesture recognition.

PURPOSE: The use of motion sensors is emerging as a means for measuring surgical performance. Motion sensors are typically used for calculating performance metrics and assessing skill. The aim of this study was to identify surgical gestures and tools used during an open surgery suturing simulation based on motion sensor data. METHODS: Twenty-five participants performed a suturing task on a variable tissue simulator. Electromagnetic motion sensors were used to measure their performance. The current study compares GRU and LSTM networks, which are known to perform well on other kinematic datasets, as well as MS-TCN++, which was developed for video data and was adapted in this work for motion sensors data. Finally, we extended all architectures for multi-tasking. RESULTS: In the gesture recognition task the MS-TCN++ has the highest performance with accuracy of [Formula: see text] and F1-Macro of [Formula: see text], edit distance of [Formula: see text] and F1@10 of [Formula: see text] In the tool usage recognition task for the right hand, MS-TCN++ performs the best in most metrics with an accuracy score of [Formula: see text], F1-Macro of [Formula: see text], F1@10 of [Formula: see text], and F1@25 of [Formula: see text]. The multi-task GRU performs best in all metrics in the left-hand case, with an accuracy of [Formula: see text], edit distance of [Formula: see text], F1-Macro of [Formula: see text], F1@10 of [Formula: see text], and F1@25 of [Formula: see text]. CONCLUSION: In this study, using motion sensor data, we automatically identified the surgical gestures and the tools used during an open surgery suturing simulation. Our methods may be used for computing more detailed performance metrics and assisting in automatic workflow analysis. MS-TCN++ performed better in gesture recognition as well as right-hand tool recognition, while the multi-task GRU provided better results in the left-hand case. It should be noted that our multi-task GRU network is significantly smaller and has achieved competitive results in the rest of the tasks as well.

Video-based fully automatic assessment of open surgery suturing skills.

PURPOSE: The goal of this study was to develop a new reliable open surgery suturing simulation system for training medical students in situations where resources are limited or in the domestic setup. Namely, we developed an algorithm for tools and hands localization as well as identifying the interactions between them based on simple webcam video data, calculating motion metrics for assessment of surgical skill. METHODS: Twenty-five participants performed multiple suturing tasks using our simulator. The YOLO network was modified to a multi-task network for the purpose of tool localization and tool-hand interaction detection. This was accomplished by splitting the YOLO detection heads so that they supported both tasks with minimal addition to computer run-time. Furthermore, based on the outcome of the system, motion metrics were calculated. These metrics included traditional metrics such as time and path length as well as new metrics assessing the technique participants use for holding the tools. RESULTS: The dual-task network performance was similar to that of two networks, while computational load was only slightly bigger than one network. In addition, the motion metrics showed significant differences between experts and novices. CONCLUSION: While video capture is an essential part of minimal invasive surgery, it is not an integral component of open surgery. Thus, new algorithms, focusing on the unique challenges open surgery videos present, are required. In this study, a dual-task network was developed to solve both a localization task and a hand-tool interaction task. The dual network may be easily expanded to a multi-task network, which may be useful for images with multiple layers and for evaluating the interaction between these different layers.