ArticleSurgical endoscopy2022
Multicentric validation of EndoDigest: a computer vision platform for video documentation of the critical view of safety in laparoscopic cholecystectomy.
Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Detection of anatomic landmarks during laparoscopic cholecystectomy with the use of artificial intelligence-a systematic review of the literature.Updates in surgery · 2026Pooled it
- Systematic review on the use of artificial intelligence to identify anatomical structures during laparoscopic cholecystectomy: a tool towards the future.Langenbeck's archives of surgery · 2025Pooled it
- A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.Surgical endoscopy · 2023Pooled it
- Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future Directions.Medicina (Kaunas, Lithuania) · 2026Review
- GEN-Guard: correcting generalization failures for deployable federated surgical AI.International journal of computer assisted radiology and surgery · 2026Article
- Artificial intelligence in gastrointestinal and hepatopancreatobiliary surgery: innovations, integration, and future directions.Annals of medicine and surgery (2012) · 2026Review
- An artificial intelligence-based navigation system for enhancing anatomical recognition and safety during laparoscopic cholecystectomy: a pilot study.International journal of surgery (London, England) · 2026Article
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery.Mayo Clinic proceedings. Digital health · 2025Article
- Endoscopic sleeve gastroplasty video assessment: do technical features influence ESG integrity and weight loss at 6 and 12 months follow-up?Surgical endoscopy · 2025Article
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Automated assessment of simulated laparoscopic surgical skill performance using deep learning.Scientific reports · 2025Article
- Article
- Endoscapes, a critical view of safety and surgical scene segmentation dataset for laparoscopic cholecystectomy.Scientific data · 2025Article
- Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.International journal of computer assisted radiology and surgery · 2024Article
- Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos.Scientific reports · 2023Article
- SAGES video acquisition framework-analysis of available OR recording technologies by the SAGES AI task force.Surgical endoscopy · 2023Article
- Automatic generation of operation notes in endoscopic pituitary surgery videos using workflow recognition.Intelligence-based medicine · 2023Article
- Computer vision in surgery: from potential to clinical value.NPJ digital medicine · 2022Review
- Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives.Cancers · 2022Review
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA computer vision (CV) platform named EndoDigest was recently developed to facilitate the use of surgical videos. Specifically, EndoDigest automatically provides short video clips to effectively document the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). The aim of the present study is to validate EndoDigest on a multicentric dataset of LC videos.
methodsLC videos from 4 centers were manually annotated with the time of the cystic duct division and an assessment of CVS criteria. Incomplete recordings, bailout procedures and procedures with an intraoperative cholangiogram were excluded. EndoDigest leveraged predictions of deep learning models for workflow analysis in a rule-based inference system designed to estimate the time of the cystic duct division. Performance was assessed by computing the error in estimating the manually annotated time of the cystic duct division. To provide concise video documentation of CVS, EndoDigest extracted video clips showing the 2 min preceding and the 30 s following the predicted cystic duct division. The relevance of the documentation was evaluated by assessing CVS in automatically extracted 2.5-min-long video clips.
results144 of the 174 LC videos from 4 centers were analyzed. EndoDigest located the time of the cystic duct division with a mean error of 124.0 ± 270.6 s despite the use of fluorescent cholangiography in 27 procedures and great variations in surgical workflows across centers. The surgical evaluation found that 108 (75.0%) of the automatically extracted short video clips documented CVS effectively.
conclusionsEndoDigest was robust enough to reliably locate the time of the cystic duct division and efficiently video document CVS despite the highly variable workflows. Training specifically on data from each center could improve results; however, this multicentric validation shows the potential for clinical translation of this surgical data science tool to efficiently document surgical safety.
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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.