Observational studyEndoscopy2022
Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination.
Observational study in Endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Colorectal mucosal exposure area assessment using artificial intelligence: a multicenter prospective observational study.Endoscopy · 2026Trial
- Automatic Quality Control System and Adenoma Detection Rates During Routine Colonoscopy: A Randomized Clinical Trial.JAMA network open · 2025Trial
- External Validation of an AI-Based Colonoscopy Quality Assessment Tool in Japanese Clinical Practice: A Pilot Study.Digestive diseases and sciences · 2026Article
- Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Human-machine collaboration in colorectal cancer screening: a narrative review of artificial intelligence-assisted colonoscopy.Frontiers in medicine · 2026Review
- Asia-Pacific consensus on the use of artificial intelligence in colorectal cancer screening and surveillance.iGIE : innovation, investigation and insights · 2025Article
- Impact of withdrawal time on the adenoma detection rate in elderly patients during unsedated colonoscopy: a retrospective multicenter study.BMC gastroenterology · 2025Article
- Clinical significance of computer-aided quality assessment systems in colonoscopy: a comprehensive review.Clinical endoscopy · 2025Review
- Artificial Intelligence in Endoscopic and Ultrasound Imaging for Inflammatory Bowel Disease.Journal of clinical medicine · 2025Review
- Artificial intelligence alert system based on intraluminal view for colonoscopy intubation.Scientific reports · 2025Article
- [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment.Journal of imaging · 2025Article
- Explaining the Correlates of the Multi-Theory Model (MTM) of Health Behavior Change in Visual (Structural) Colorectal Cancer Screening Examinations.International journal of environmental research and public health · 2025Article
- Review
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- As how artificial intelligence is revolutionizing endoscopy.Clinical endoscopy · 2024Review
- Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.Endoscopy · 2023Article
- Review
- Withdrawal time in colonoscopy, past, present, and future, a narrative review.Translational gastroenterology and hepatology · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system's evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system's ability to improve FEQ during colonoscopy.
methodsFirst, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the system's performance in enhancing fold examination.
resultsThe system's evaluations of FEQ of each endoscopist were significantly correlated with experts' scores (r = 0.871,
conclusionThe system's evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ.
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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.