Previously submitted to: JMIR Medical Education (no longer under consideration since Dec 03, 2024)
Date Submitted: Aug 29, 2023
Open Peer Review Period: Aug 29, 2023 - Oct 24, 2023
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Towards objective and explainable assessment of surgical skill with deep learning
ABSTRACT
Background:
Currently, evaluating surgical technical performance is inefficient and subjective and the established rubrics for assessing surgical ability are open to interpretation. To power programs for surgical training and Maintenance of Certification (MOC), a reliable and validated solution is required.
Objective:
We draw upon recent advances in machine learning and propose a framework for objective and scalable assessment of technical proficiency.
Methods:
Different machine learning models were trained to predict surgical performance on the public EndoVis19 and JIGSAWS datasets. The most important features were extracted by probing each machine learning model, and these features form the basis of the proposed algorithm. We internally tested the performance of this model on proprietary datasets from Surgical Safety Technologies (SST) and the University of Texas Southwestern (UTSW). The performance of these models was assessed according to various statistical techniques such as precision, recall, f1-scores and the area under the receiver operating characteristic curve (AUC).
Results:
OR Vision is a statistically-driven multi-stage machine learning tool that quantifies surgical skill objectively and explainably. Instrument motion, control, and coordination are quantified in terms of 150 objective metrics, extracted from tool motion tracked by the deep learning model. The N most highly correlated of these metrics (p<0.05) model surgical performance with quantifiable objective metrics (fine-motor precision, fluidity, tremor, disorder, etc.). These metrics are combined into clinically-relevant composite scores that represent the technical performance of surgeons. The OR Vision score discriminates between expert and novice surgeons with high precision (0.82-0.84) and provides constructive feedback in the form of a concise report for every participating member of the cohort. Each report provides a breakdown of user performance on statistically relevant categories.
Conclusions:
A machine learning-based approach for identifying surgical skill is effective and meaningful and provides the groundwork for objective, precise, repeatable, cost-effective, clinically-meaningful assessments.
Citation
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