Automation of surgical skill assessment using a three-stage machine learning algorithm

Lavanchy, Joël L.; Zindel, Joel; Kirtac, Kadir; Twick, Isabell; Hosgor, Enes; Candinas, Daniel; Beldi, Guido (2021). Automation of surgical skill assessment using a three-stage machine learning algorithm. Scientific reports, 11(1) Springer Nature 10.1038/s41598-021-84295-6

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Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.

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