Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · 2026
The yield of artificial intelligence (GI genius) in Lynch syndrome -A randomized tandem-colonoscopy trial.
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Ido Laish, Shomron Ben-Horin, Idan Levi, Ukashi Offir
Аннотация
BACKGROUND AND AIMS: Artificial intelligence (AI)- assisted colonoscopy has been shown to increase the adenoma-detection rate in the general population but there is a paucity of data on its benefit in Lynch syndrome. We aimed to investigate the incremental detection rate of polyps using AI- assisted colonoscopy compared with high-definition white-light endoscopy (HD-WLE).
METHODS: We conducted a single-center randomized tandem colonoscopy trial of patients with Lynch syndrome. Patients underwent a first colonoscopy with HD-WLE, and were randomized at 1:1 ratio to undergo a second colonoscopy with either HD-WLE or an AI- assisted device. All visualized polyps were removed and size, histology and numbers of polyps detected on each exam were recorded.
RESULTS: Of 107 patients screened, 100 were enrolled (median age 45.5 years, 39% males). On a per polyp analysis, 12 adenomas were detected at the first colonoscopy and 6 at the second one in the AI group, corresponding to incremental detection rate (IDR) of 33.3%, whereas in the HD-WLE group, 16 adenomas were detected at first colonoscopy and 2 at second, corresponding to IDR of 11.1% [OR 4.0, 95% CI (0.68-23.40); p value- 0.124]. On per patient analysis, the increment in adenoma detection rate and polyp detection rate at second colonoscopy were also not significantly increased with AI compared with HD-WLE (35.3%vs. 13.3%, p - 0.165; and 26.6% vs. 14.8%, p - 0.278; respectively).
CONCLUSIONS: No significant difference was displayed by artificial intelligence in detecting adenomas in Lynch-syndrome patients in the setting of high-definition colonoscopy with prolonged withdrawal time.
METHODS: We conducted a single-center randomized tandem colonoscopy trial of patients with Lynch syndrome. Patients underwent a first colonoscopy with HD-WLE, and were randomized at 1:1 ratio to undergo a second colonoscopy with either HD-WLE or an AI- assisted device. All visualized polyps were removed and size, histology and numbers of polyps detected on each exam were recorded.
RESULTS: Of 107 patients screened, 100 were enrolled (median age 45.5 years, 39% males). On a per polyp analysis, 12 adenomas were detected at the first colonoscopy and 6 at the second one in the AI group, corresponding to incremental detection rate (IDR) of 33.3%, whereas in the HD-WLE group, 16 adenomas were detected at first colonoscopy and 2 at second, corresponding to IDR of 11.1% [OR 4.0, 95% CI (0.68-23.40); p value- 0.124]. On per patient analysis, the increment in adenoma detection rate and polyp detection rate at second colonoscopy were also not significantly increased with AI compared with HD-WLE (35.3%vs. 13.3%, p - 0.165; and 26.6% vs. 14.8%, p - 0.278; respectively).
CONCLUSIONS: No significant difference was displayed by artificial intelligence in detecting adenomas in Lynch-syndrome patients in the setting of high-definition colonoscopy with prolonged withdrawal time.