Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study.

BACKGROUND AND AIMS: Computer-assisted detection (CADe) is a promising technologic advance that enhances adenoma detection during colonoscopy. However, the role of CADe in reducing missed colonic lesions is uncertain. The aim of this study was to determine the miss rates of proximal colonic lesions by CADe and conventional colonoscopy. METHODS: This was a prospective, multicenter, randomized, tandem-colonoscopy study conducted in 3 Asian centers. Patients were randomized to receive CADe or conventional white-light colonoscopy during the first withdrawal of the proximal colon (cecum to splenic flexure), immediately followed by tandem examination of the proximal colon with white light in both groups. The primary outcome was adenoma/polyp miss rate, which was defined as any adenoma/polyp detected during the second examination. RESULTS: Of 223 patients (48.6% men; median age, 63 years) enrolled, 7 patients did not have tandem examination, leaving 108 patients in each group. There was no difference in the miss rate for proximal adenomas (CADe vs conventional: 20.0% vs 14.0%, P = .07) and polyps (26.7% vs 19.6%, P = .06). The CADe group, however, had significantly higher proximal polyp (58.0% vs 46.7%, P = .03) and adenoma (44.7% vs 34.6%, P = .04) detection rates than the conventional group. The mean number of proximal polyps and adenomas detected per patient during the first examination was also significantly higher in the CADe group (polyp: 1.20 vs .86, P = .03; adenoma, .91 vs .61, P = .03). Subgroup analysis showed that CADe enhanced proximal adenoma detection in patients with fair bowel preparation, shorter withdrawal time, and endoscopists with lower adenoma detection rate. CONCLUSIONS: This multicenter trial from Asia confirmed that CADe can further enhance proximal adenoma and polyp detection but may not be able to reduce the number of missed proximal colonic lesions. (Clinical trial registration number: NCT04294355.).

New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video).

BACKGROUND AND AIMS: Meta-analysis shows that up to 26% of adenomas could be missed during colonoscopy. We investigated whether the use of artificial intelligence (AI)-assisted real-time detection could provide new insights into mechanisms underlying missed lesions during colonoscopy. METHODS: A validated real-time deep-learning AI model for the detection of colonic polyps was first tested in videos of tandem colonoscopy of the proximal colon for missed lesions. The real-time AI model was then prospectively validated in a total colonoscopy in which the endoscopist was blinded to real-time AI findings. Segmental unblinding of the AI findings were provided, and the colonic segment was then re-examined when missed lesions were detected by AI but not the endoscopist. All polyps were removed for histologic examination as the criterion standard. RESULTS: Sixty-five videos of tandem examination of the proximal colon were reviewed by AI. AI detected 79.1% (19/24) of missed proximal adenomas in the video of the first-pass examination. In 52 prospective colonoscopies, real-time AI detection detected at least 1 missed adenoma in 14 patients (26.9%) and increased the total number of adenomas detected by 23.6%. Multivariable analysis showed that a missed adenoma(s) was more likely when there were multiple polyps (adjusted odds ratio, 1.05; 95% confidence interval, 1.02-1.09; P < .0001) or colonoscopy was performed by less-experienced endoscopists (adjusted odds ratio, 1.30; 95% confidence interval, 1.05-1.62; P = .02). CONCLUSIONS: Our findings provide new insights on the prominent role of human factors, including inexperience and distraction, on missed colonic lesions. With the use of real-time AI assistance, up to 80% of missed adenomas could be prevented. (Clinical trial registration number: NCT04227795.).