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Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review

Objective: To review artificial intelligence (AI) based applications for the assessment of technical skills in minimally invasive surgery. Background: As technical skill assessment in surgery relies on expert opinion, it is time-consuming, costly, and often lacks objectivity. Analysis of routinely generated data by AI methods has the potential for automatic technical skill assessment in minimally invasive surgery. Methods: A systematic search of Medline, Embase, Web of Science and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Results: In total, 1467 articles were identified, 37 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (49%) or kinematic data from robotic systems or sensors (46%) were the most frequent input data for AI. Most studies used deep learning (73%) and predicted technical skills using an ordinal assessment scale (73%) with good accuracies in simulated settings. However, all proposed models were in development stage, only 8% were externally validated and 16% showed a low RoB. Conclusion: AI is promising to automate technical skill assessment in minimally invasive surgery. However, models should be benchmarked on representative datasets using predefined performance metrics and tested in clinical implementation studies.