Real-Time AI-Assisted Detection of Colonic Lesions Using the iIDEAS Intelligent-C Module: A Prospective Single-Center Validation Study.

AIMS: Colorectal cancer (CRC) remains a major cause of cancer mortality, with up to 26% of adenomas missed on conventional colonoscopy. Artificial intelligence (AI)-based computer-aided detection (CADe) systems may reduce these miss rates. We aimed to validate the iIDEAS-C CADe module in a prospective cohort, with co-primary endpoints of adenomas per colonoscopy (APC) and lesion-level miss rate. METHODS AND RESULTS: In this prospective, nonrandomized, single-center study (NCT05784935), consecutive adults (18-75 years) undergoing screening, surveillance, or diagnostic colonoscopy were enrolled between February and May 2023. Each colon segment was inspected twice: an initial blinded withdrawal without CADe, followed by a second inspection with iIDEAS-C. Missed lesions were those identified by CADe during the second inspection and confirmed endoscopically. Secondary outcomes included PDR, ADR, PPC, lesion characteristics, and procedural times.Two hundred patients (mean age 44.1 ± 12.7 years; 64% male) were included. Eighty-two polyps were detected across 42 patients (PDR = 21%). Endoscopists detected 61/82 polyps (74.4%), yielding a per-polyp miss rate of 25.6%. The iIDEAS-C module detected all 82 lesions, achieving 0% per-polyp and per-adenoma miss rate. Missed lesions were mostly diminutive and proximal. PPC increased from 0.305 to 0.410 (p < 0.0001), PDR from 17.5% to 21.0% (p = 0.016), and APC from 0.115 to 0.140 (p = 0.029). No adverse events occurred. CONCLUSION: The iIDEAS-C module demonstrated a 0% lesion-level miss rate and significantly improved key detection metrics, especially for small proximal lesions. Larger multicenter studies are warranted to confirm these findings and assess long-term outcomes. ClinicalTrials.gov: NCT05784935. Trial Registration: ClinicalTrials.gov identifier: NCT05784935.