ArticleInternational journal of colorectal disease2021
An analysis about the function of a new artificial intelligence, CAD EYE with the lesion recognition and diagnosis for colorectal polyps in clinical practice.
Article in International journal of colorectal disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05542030 (Accuracy of CAD Eye in the Detection of Colonic Remaining Lesions After Endoscopic Mucosal Resection), which is not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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.
Accuracy of CAD Eye in the Detection of Colonic Remaining Lesions After Endoscopic Mucosal Resection: a Pilot Study
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computer-aided diagnosis for colorectal polyp in comparison with endoscopists: Systematic review and meta-analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Pooled it
- Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2024Trial
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Linked color imaging versus artificial intelligence-assisted linked color imaging for neoplasia detection in the colorectum: a randomized trial in Brazil.Clinical endoscopy · 2026Article
- Development of an Artificial Intelligence for Detecting False Positive Detection to Manage the Accuracy of Artificial Intelligence in Colonoscopy.Journal of the anus, rectum and colon · 2026Article
- Comparison of colon adenoma detection rate using cap-assisted and artificial intelligence-assisted colonoscopy at a tertiary hospital in the Philippines: a propensity score-matched analysis.Clinical endoscopy · 2026Article
- Evaluation of computer-aided detection for gastric cancer using white-light and linked-color imaging: a pilot study.iGIE : innovation, investigation and insights · 2025Article
- [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa.Frontiers in pediatrics · 2025Review
- Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps.Journal of the anus, rectum and colon · 2025Article
- Multicentre study to assess the performance of an artificial intelligence instrument to support qualitative diagnosis of colorectal polyps.BMJ open gastroenterology · 2024Article
- Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study.Endoscopy international open · 2024Article
- Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets.Journal of the anus, rectum and colon · 2024Article
- Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging.Journal of the anus, rectum and colon · 2024Article
- The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization.Cancers · 2023Review
- Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center.Endoscopy international open · 2023Article
- Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials.Clinical endoscopy · 2023Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Mucosal imaging in colon polyps: New advances and what the future may hold.World journal of gastroenterology · 2022Review
- Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesRecently, CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for the lesion recognition (CADe) and the optical diagnosis (CADx) of colorectal polyps, was released. We evaluated the function of CADe and CADx of CAD EYE.
methodsIn this single-center retrospective study, we examined consecutive polyps ≤ 10 mm detected from March to April 2021 to determine whether CAD EYE could recognize them live with both normal- and high-speed observation using white-light imaging (WLI) and linked-color imaging (LCI). We then examined whether the polyps were neoplastic or hyperplastic live with magnified or non-magnified blue-laser imaging (BLI-LASER) or blue-light imaging (BLI-LED) under CAD EYE, comparing the retrospective evaluations with 5 experts and 5 trainees using still images. All polyps were histopathologically examined.
resultsWe analyzed 100 polyps (mean size 3.9 ± 2.6 mm; 55 neoplastic and 45 hyperplastic lesions) in 25 patients. Regarding CADe, the respective detection rates of CAD EYE with normal- and high-speed observation were 85.0% and 67.0% for WLI (p = 0.002) and 89.0% and 75.0% for LCI (p = 0.009). Regarding CADx for differentiating neoplastic and hyperplastic lesions, the diagnostic accuracy values of CAD EYE with non-magnified and magnified BLI-LASER/LED were 88.8% and 87.8%. Regarding magnified BLI-LASER/LED, the diagnostic accuracy value of CAD EYE was not significantly different from that of experts (92.0%, p = 0.17), but that of trainees (79.0%, p = 0.04). We also found no significant differences in CADe or CADx between LED (53 lesions) and LASER (47 lesions).
conclusionsCAD EYE was a helpful tool for CADe and CADx in clinical practice.
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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.