ArticleEndoscopy2023
Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.
Article in Endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed.
- A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement.Endoscopy · 2026Trial
- Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update.Diagnostics (Basel, Switzerland) · 2026Review
- Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.NPJ digital medicine · 2026Article
- Evaluation of Net Withdrawal Time and Colonoscopy Video Summarization Using Deep Learning Based Automated Temporal Video Segmentation.Journal of imaging informatics in medicine · 2026Article
- Assessing realism of artificial intelligence-generated colorectal polyp images: International multicenter blinded reader study.Endoscopy international open · 2026Article
- Human-machine collaboration in colorectal cancer screening: a narrative review of artificial intelligence-assisted colonoscopy.Frontiers in medicine · 2026Review
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Artificial Intelligence-Assisted Flexure and Landmark Identification Improves Reporting Completeness in Colonoscopy.Endoscopy international open · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- An artificial intelligence system for qualified mucosal observation time during colonoscopic withdrawal.NPJ digital medicine · 2025Article
- Usefulness of an artificial intelligence-based colonoscopy report generation support system.Clinical endoscopy · 2025Article
- Prospective Evaluation of Real-Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction.United European gastroenterology journal · 2025Article
- Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.PloS one · 2025Article
- Implementing endoscopy video recording in routine clinical practice: Strategies from three tertiary care centers.Endoscopy international open · 2025Article
- Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.Scientific reports · 2024Article
- Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation.Endoscopy international open · 2024Article
- Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices.Digestion · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundReliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation.
methodA multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10 557 images (1300 examinations, nine centers, four processors). Consecutively, the algorithm was used to calculate withdrawal time (AI prediction) and extract relevant images. Validation was performed on 100 colonoscopy videos (five centers). The reported and AI-predicted withdrawal times were compared with video-based measurement; photodocumentation was compared for documented polypectomies.
resultsVideo-based measurement in 100 colonoscopies revealed a median absolute difference of 2.0 minutes between the measured and reported withdrawal times, compared with 0.4 minutes for AI predictions. The original photodocumentation represented the cecum in 88 examinations compared with 98/100 examinations for the AI-generated documentation. For 39/104 polypectomies, the examiners' photographs included the instrument, compared with 68 for the AI images. Lastly, we demonstrated real-time capability (10 colonoscopies).
conclusionOur AI system calculates withdrawal time, provides an image report, and is real-time ready. After further validation, the system may improve standardized reporting, while decreasing the workload created by routine documentation.
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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.