Efficacy of artificial intelligence in detecting precancerous lesions among a colorectal cancer screening population undergoing surveillance colonoscopies in Italy: a randomized control trial (GENIAL-CO Follow-Up study).

BACKGROUND AND AIMS: Computer-aided detection (CADe) has demonstrated improved adenoma detection rate (ADR) in screening populations; however, its value in post-polypectomy surveillance colonoscopy remains unclear. This study evaluated CADe efficacy in enhancing detection metrics during surveillance colonoscopy within an organized colorectal cancer (CRC) screening program. METHODS: In this multicenter randomized controlled trial, patients aged 50-74 scheduled for surveillance colonoscopy were randomized 1:1 to CADe-assisted or standard colonoscopy (SC). The primary endpoint was ADR. Secondary endpoints included advanced ADR, adenomas per colonoscopy (APC), sessile serrated lesion detection rate (SSLDR), and withdrawal time (WT). RESULTS: Of 973 enrolled patients, 914 were included after excluding 59 with inadequate bowel preparation (CADe: 455 vs SC: 459). ADR and AADR did not differ significantly between groups (66.6%vs 61.0%, p = 0.160; 7.3% vs 6.3%, p = 0.263). APC was significantly higher in the CADe group (1.80±2.19 vs 1.32±1.75, p < 0.001), while SSLDR was comparable (17.1%vs 14.2%, p = 0.154). Mean WT was significantly longer with CADe (16.35±8.32 vs 14.52±7.24 min, p < 0.001). CONCLUSIONS: CADe-assisted colonoscopy did not improve ADR or AADR in a CRC organized screening population undergoing surveillance colonoscopy, while significantly increasing WT.