ArticleNature communications2023
Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07392268 (Clinical Utility and Validation of Novel AI-ENDO System in Endoscopic Submucosal Dissection), which is not on this map. Cited by 19 papers.
What it found
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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.
Clinical Utility and Validation of Novel AI-ENDO System in Endoscopic Submucosal Dissection
Who cites it
19 citing papers in PubMed, 36 citations in OpenAlex.
- Artificial intelligence-based phase recognition for workflow analysis in robotic choledochal cyst excision: a proof-of-concept study.Surgical endoscopy · 2026Article
- Cooperative integrated surgical robots for high-performance targeted therapies.Innovation (Cambridge (Mass.)) · 2026Review
- DSTED: decoupling temporal stabilization and discriminative enhancement for surgical workflow recognition.International journal of computer assisted radiology and surgery · 2026Article
- [Clinical Implementation of Artificial Intelligence in Endoscopy: A Human-Artificial Intelligence Interaction Perspective].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
- Deep learning-driven multi-hierarchical granularity integration for surgical scene understanding: experimental study.International journal of surgery (London, England) · 2026Article
- Renji endoscopic submucosal dissection video data set for Esophagus.Scientific data · 2025Article
- External Validation of an Automated Surgical Step Recognition Model for Robotic Distal Gastrectomy (RDG) Using a Multicenter Dataset.Annals of gastroenterological surgery · 2025Article
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- The Cost-Effectiveness of AI-Assisted Colonoscopy as a Primary or Secondary Screening Test in a Population-Based Colorectal Cancer Screening Program: Markov Modeling-Based Cost Effectiveness Analysis.Journal of medical Internet research · 2025Article
- Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions.Scientific data · 2025Article
- Artificial intelligence-based marks detection and incision guide line prediction model in esophageal endoscopic submucosal dissection: a multicenter study (with video).Surgical endoscopy · 2025Article
- Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video).Surgical endoscopy · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Renji endoscopic submucosal dissection video data set for early gastric cancer.Scientific data · 2025Article
- Surgical video workflow analysis via visual-language learning.npj health systems · 2025Article
- Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery.NPJ digital medicine · 2025Article
- LC-YOLOmatch: a novel scene segmentation approach based on YOLO for laparoscopic cholecystectomy.Frontiers in artificial intelligence · 2025Article
- Does development of submucosal dissection models influence quality of training? Comparison of existing models.Endoscopy international open · 2025Article
- Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study.Nature communications · 2023Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in artificial intelligence have witnessed human-level performance; however, AI-enabled cognitive assistance for therapeutic procedures has not been fully explored nor pre-clinically validated. Here we propose AI-Endo, an intelligent surgical workflow recognition suit, for endoscopic submucosal dissection (ESD). Our AI-Endo is trained on high-quality ESD cases from an expert endoscopist, covering a decade time expansion and consisting of 201,026 labeled frames. The learned model demonstrates outstanding performance on validation data, including cases from relatively junior endoscopists with various skill levels, procedures conducted with different endoscopy systems and therapeutic skills, and cohorts from international multi-centers. Furthermore, we integrate our AI-Endo with the Olympus endoscopic system and validate the AI-enabled cognitive assistance system with animal studies in live ESD training sessions. Dedicated data analysis from surgical phase recognition results is summarized in an automatically generated report for skill assessment.
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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.