Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video)

Background: Endoscopic submucosal dissection (ESD) is a crucial yet challenging multi-phase procedure for treating early gastrointestinal cancers. This study developed an artificial intelligence (AI)-based automated surgical workflow recognition model for esophageal ESD and proposed an innovative training program based on esophageal ESD videos with or without AI labels to evaluate its effectiveness for trainees. Methods: We retrospectively analyzed complete ESD videos collected from seven hospitals worldwide between 2016 and 2024. The ESD surgical workflow was divided into 6 phases and these videos were divided into five datasets for AI model. Trainees were invited to participate in this multimedia training program and were assigned to the AI or control group randomly. The performance of the AI model and label testing were evaluated using the accuracy. Results: A total of 195 ESD videos (782,488 s, 9268 phases) were included. The AI model achieved accuracy of 92.08% (95% confidence interval (CI), 91.40–92.76%), 91.71% (95% CI 90.11–93.31%), and 89.84% (95% CI 87.42–92.25%) in the training, internal, and external test dataset (esophagus), respectively. It also achieved acceptable results in the external test dataset (stomach, colorectum). For the training program, the overall label testing accuracy of the AI group learning ESD videos with AI labels was 88.73 ± 2.97%, significantly higher than the control group without AI labels (81.51 ± 4.63%, P < 0.001). Conclusion: The AI model achieved high accuracy in the large ESD video datasets. The training program improves understanding of the complexity of ESD workflow and demonstrates the program’s effectiveness for trainees.