Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial
Abstract
Background and aims Artificial intelligence-based computer-aid detection (CADe) devices have been recently tested in colonoscopies, increasing the adenoma detection rate (ADR), mainly in Asian populations. However, evidence for the benefit of these devices in the occidental population is still low. We tested a new CADe device, namely, ENDO-AID (OIP-1) (Olympus, Tokyo, Japan), in clinical practice. Methods This randomized controlled trial included 370 consecutive patients who were randomized 1:1 to CADe (n = 185) versus standard exploration (n = 185) from November 2021 to January 2022. The primary endpoint was the ADR. Advanced adenoma was defined as ≥10 mm, harboring high-grade dysplasia, or with a villous pattern. Otherwise, the adenoma was nonadvanced. ADR was assessed in both groups stratified by endoscopist ADR and colon cleansing. Results In the intention-to-treat analysis, the ADR was 55.1% (102/185) in the CADe group and 43.8% (81/185) in the control group (P = .029). Nonadvanced ADRs (54.8% vs 40.8%, P = .01) and flat ADRs (39.4 vs 24.8, P = .006), polyp detection rate (67.1% vs 51%; P = .004), and number of adenomas per colonoscopy were significantly higher in the CADe group than in the control group (median [25th-75th percentile], 1 [0-2] vs 0 [0-1.5], respectively; P = .014). No significant differences were found in serrated ADR. After stratification by endoscopist and bowel cleansing, no statistically significant differences in ADR were found. Conclusions Colonosc…
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CADe systems prove particularly beneficial in aiding the detection of small and flat lesions.
“CADe systems prove particularly beneficial in aiding the detection of these challenging lesions [76 , 82 , 83] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [82]
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Adenoma detection rate and risk of colorectal cancer and death
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Enhancing human-AI collaboration: The case of colonoscopy
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- Authors
- Gimeno-García, A. Z., Hernández Negrin, D., Hernández, A., Nicolás-Pérez, D., Rodríguez, E., Montesdeoca, C., Alarcon, O., Romero, R., Baute Dorta, J. L., Cedrés, Y., Castillo, R. D., Jiménez, A., Felipe, V., Morales, D., Ortega, J., Reygosa, C., Quintero, E., & Hernández-Guerra, M.
- Affiliations
- Universidad de La Laguna (ES)
- Hospital Universitario de Canarias (ES)
- Venue
- Gastrointestinal Endoscopy
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 97
- Issue
- 3
- Pages
- 528-536.e1
- ISSN
- 0016-5107 (ISSN-L)
- OpenAlex
- W4304124022
- Cited by
- 78
- References
- 2
- Citations by year
- 2026: 19 · 2025: 24 · 2024: 17 · 2023: 18
- FWCI
- 6.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Gastrointestinal Endoscopy (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
- Keywords
- computer-aided detection, colonoscopy, adenoma detection rate, colorectal neoplasia, advanced adenoma, diminutive adenomas, flat adenomas, polyp detection, serrated lesions, bowel preparation, Western populations
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Polyps, Colorectal Neoplasms
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