Importance: When evaluating surgeons in the operating room, experienced physicians must rely on live or recorded video to assess the surgeon's technical performance, an approach prone to subjectivity and error. Owing to the large number of surgical procedures performed daily, it is infeasible to review every procedure; therefore, there is a tremendous loss of invaluable performance data that would otherwise be useful for improving surgical safety. Objective: To evaluate a framework for assessing surgical video clips by categorizing them based on the surgical step being performed and the level of the surgeon's competence. Design, Setting, and Participants: This quality improvement study assessed 103 video clips of 8 surgeons of various levels performing knot tying, suturing, and needle passing from the Johns Hopkins University-Intuitive Surgical Gesture and Skill Assessment Working Set. Data were collected before 2015, and data analysis took place from March to July 2019. Main Outcomes and Measures: Deep learning models were trained to estimate categorical outputs such as performance level (ie, novice, intermediate, and expert) and surgical actions (ie, knot tying, suturing, and needle passing). The efficacy of these models was measured using precision, recall, and model accuracy. Results: The provided architectures achieved accuracy in surgical action and performance calculation tasks using only video input. The embedding representation had a mean (root mean square error [RMSE]) precision of 1.00 (0) for suturing, 0.99 (0.01) for knot tying, and 0.91 (0.11) for needle passing, resulting in a mean (RMSE) precision of 0.97 (0.01). Its mean (RMSE) recall was 0.94 (0.08) for suturing, 1.00 (0) for knot tying, and 0.99 (0.01) for needle passing, resulting in a mean (RMSE) recall of 0.98 (0.01). It also estimated scores on the Objected Structured Assessment of Technical Skill Global Rating Scale categories, with a mean (RMSE) precision of 0.85 (0.09) for novice level, 0.67 (0.07) for intermediate level, and 0.79 (0.12) for expert level, resulting in a mean (RMSE) precision of 0.77 (0.04). Its mean (RMSE) recall was 0.85 (0.05) for novice level, 0.69 (0.14) for intermediate level, and 0.80 (0.13) for expert level, resulting in a mean (RMSE) recall of 0.78 (0.03). Conclusions and Relevance: The proposed models and the accompanying results illustrate that deep machine learning can identify associations in surgical video clips. These are the first steps to creating a feedback mechanism for surgeons that would allow them to learn from their experiences and refine their skills.
Importance: When evaluating surgeons in the operating room, experienced physicians must rely on live or recorded video to assess the surgeon's technical performance, an approach prone to subjectivity and error. Owing to the large number of surgical procedures performed daily, it is infeasible to review every procedure; therefore, there is a tremendous loss of invaluable performance data that would otherwise be useful for improving surgical safety. Objective: To evaluate a framework for assessing surgical video clips by categorizing them based on the surgical step being performed and the level of the surgeon's competence. Design, Setting, and Participants: This quality improvement study assessed 103 video clips of 8 surgeons of various levels performing knot tying, suturing, and needle passing from the Johns Hopkins University-Intuitive Surgical Gesture and Skill Assessment Working Set. Data were collected before 2015, and data analysis took place from March to July 2019. Main Outcomes and Measures: Deep learning models were trained to estimate categorical outputs such as performance level (ie, novice, intermediate, and expert) and surgical actions (ie, knot tying, suturing, and needle passing). The efficacy of these models was measured using precision, recall, and model accuracy. Results: The provided architectures achieved accuracy in surgical action and performance calculation tasks using only video input. The embedding representation had a mean (root mean square error [RMSE]) precision of 1.00 (0) for suturing, 0.99 (0.01) for knot tying, and 0.91 (0.11) for needle passing, resulting in a mean (RMSE) precision of 0.97 (0.01). Its mean (RMSE) recall was 0.94 (0.08) for suturing, 1.00 (0) for knot tying, and 0.99 (0.01) for needle passing, resulting in a mean (RMSE) recall of 0.98 (0.01). It also estimated scores on the Objected Structured Assessment of Technical Skill Global Rating Scale categories, with a mean (RMSE) precision of 0.85 (0.09) for novice level, 0.67 (0.07) for intermediate level, and 0.79 (0.12) for expert level, resulting in a mean (RMSE) precision of 0.77 (0.04). Its mean (RMSE) recall was 0.85 (0.05) for novice level, 0.69 (0.14) for intermediate level, and 0.80 (0.13) for expert level, resulting in a mean (RMSE) recall of 0.78 (0.03). Conclusions and Relevance: The proposed models and the accompanying results illustrate that deep machine learning can identify associations in surgical video clips. These are the first steps to creating a feedback mechanism for surgeons that would allow them to learn from their experiences and refine their skills.
Authors: A S H M van Dalen; M Jansen; M van Haperen; S van Dieren; C J Buskens; E J M Nieveen van Dijkum; W A Bemelman; T P Grantcharov; M P Schijven Journal: Surg Endosc Date: 2020-04-06 Impact factor: 4.584
Authors: Andre Esteva; Katherine Chou; Serena Yeung; Nikhil Naik; Ali Madani; Ali Mottaghi; Yun Liu; Eric Topol; Jeff Dean; Richard Socher Journal: NPJ Digit Med Date: 2021-01-08
Authors: Kyle Lam; Junhong Chen; Zeyu Wang; Fahad M Iqbal; Ara Darzi; Benny Lo; Sanjay Purkayastha; James M Kinross Journal: NPJ Digit Med Date: 2022-03-03
Authors: Kevin Kasa; David Burns; Mitchell G Goldenberg; Omar Selim; Cari Whyne; Michael Hardisty Journal: Sensors (Basel) Date: 2022-09-27 Impact factor: 3.847