Key result
Deep learning AI predicts minimally invasive surgical skills with high accuracy but remains developmental.
Why the study?
Technical skill assessment in surgery relies on expert opinion and is time-consuming, costly, and often lacks objectivity, whereas AI methods offer potential for automatic assessment.
Does artificial intelligence accurately assess technical skills in minimally invasive surgery compared to expert assessment?
Systematic Review (n=37)
Does artificial intelligence accurately assess technical skills in minimally invasive surgery compared to expert assessment?
Artificial intelligence shows promise for automating technical skill assessment in minimally invasive surgery, but current models lack external validation and clinical implementation.
AI models for surgical skill assessment require external validation before use; leaves open their clinical role pending prospective trials.
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. Mini Abstract Technical skill assessment in minimally invasive surgery is time consuming and costly. Artificial intelligence is a promising technology to facilitate and automate technical skill assessment. Therefore, this article systematically reviews artificial intelligence applications for the assessment of technical skills in minimally invasive surgery.
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Pedrett et al. (2022) conducted a systematic review in Technical skill assessment in minimally invasive surgery (n=37). Artificial Intelligence (AI) was evaluated on Accuracy of technical skill prediction. Artificial intelligence models, predominantly using deep learning, demonstrated accuracies ranging from 0.7 to 1.0 for predicting technical skills in minimally invasive surgery, though all remain in the developmental stage.
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