Automated Methods of Technical Skill Assessment in Surgery

The goal of this study was to systematically review the literature on the use of automated methods to evaluate technical skills in surgery. The classic apprenticeship model of surgical training relies on subjective assessments, but automated methods offer a more objective, versatile, and analytical way to evaluate a trainee's technical skills. The researchers performed a literature search and reviewed 76 studies that evaluated various automated methods, including tool motion tracking, hand motion tracking, eye motion tracking, and muscle contraction analysis. These methods used kinetics, computer vision, machine learning, and deep learning to analyze surgical performance in both real and simulated environments. The studies had an average quality score of 10.86 out of 18, indicating generally high-quality research in this emerging field. The authors conclude that automated methods for assessing technical surgical skills are promising, but more research is needed to further verify and implement these techniques in surgical training programs.