AI-Generated Video Feedback to Improve Technical Skills in Coronary Artery Bypass Grafting
AI-Generated Video Feedback to Improve Technical Skills in Coronary Artery Bypass Grafting
This study aims to evaluate whether targeted video feedback generated by an artificial intelligence (AI)-based surgical performance assessment model can support improvement in technical skills among cardiac surgeons performing coronary artery bypass grafting (CABG). This is a single-group, self-controlled, pre-post interventional study. Participating surgeons will submit a baseline CABG surgical video, which will be assessed by both an AI model and blinded human expert raters using standardized scoring criteria. Based on the AI assessment, surgeons will receive personalized video feedback highlighting operative steps associated with lower technical performance. After a one-month self-directed review period, a follow-up CABG surgical video will be submitted and evaluated using the same process. Changes in human-rated technical skill scores between baseline and follow-up will be used to assess the potential educational impact of AI-generated video feedback.
Coronary artery bypass grafting (CABG) is a complex surgical procedure that requires a high level of technical skill from cardiac surgeons. Variability in surgical technique may influence procedural quality and patient outcomes. Recent advances in artificial intelligence (AI) have enabled automated assessment of surgical performance using operative video data, creating new opportunities for objective feedback and surgical education.
This study aims to evaluate whether targeted video feedback generated by an AI-based surgical performance assessment model can help cardiac surgeons improve their technical skills in CABG procedures. Participating surgeons whose baseline technical performance ranked in the lower half of the AI scoring system will receive personalized video feedback highlighting operative steps and maneuvers associated with lower performance scores.
In this single-group, self-controlled study, each participating surgeon will submit a baseline CABG surgical video, which will be independently evaluated by both the AI model and a panel of experienced cardiac surgeons using standardized scoring criteria. After receiving AI-generated video feedback, surgeons will be given one month to review and reflect on the feedback without additional formal training or coaching. A follow-up CABG surgical video will then be submitted and assessed using the same evaluation process.
The primary outcome of the study is the change in technical skill scores assigned by human expert raters between the baseline and follow-up videos. Secondary outcomes include surgeons' self-assessments of AI-identified performance deficits, agreement between AI-generated feedback and human expert feedback, and selected patient postoperative in-hospital outcomes. The findings of this study may inform the role of AI-assisted video feedback as a scalable educational tool for surgical skill development.
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zhanghaibo@fuwai.com13810483387