ArticleSurgical endoscopy2025
Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video).
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Narrative review of the ethics of artificial intelligence: are we ready for artificial intelligence in surgery?Journal of thoracic disease · 2026Review
- Renji endoscopic submucosal dissection video data set for Esophagus.Scientific data · 2025Article
- Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
backgroundEndoscopic 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.
methodsWe 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.
resultsA 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).
conclusionThe 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.
Indexed as
Identifiers
40072547What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.