Artificial intelligence-assisted colonoscopy for colorectal lesion detection: a case-control study on diagnostic accuracy and histopathological agreement.

Abstract

Background: Artificial intelligence (AI)-assisted colonoscopy has emerged as a tool to enhance adenoma detection rates (ADRs) and improve lesion characterization. However, its performance in real-world settings, especially in developing countries, remains uncertain.

Aims: The aim of this study was to evaluate the impact of AI on ADRs and its concordance with histopathological diagnosis.

Methods: A matched case-control study was conducted at a colorectal cancer (CRC) referral center, including 146 patients aged 45-75 years who underwent colonoscopy for CRC screening or surveillance. Patients were allocated into two groups: AI-assisted colonoscopy (n=74) and high-definition conventional colonoscopy (n=72). The primary outcome was ADR, and the secondary outcome was the agreement between AI-based lesion characterization and histopathology. Statistical analysis was performed with a significance level of p<0.05.

Results: ADR was higher in the AI group (60%) than in the control group (50%), but this difference was not statistically significant (p>0.05). AI-assisted lesion characterization showed substantial agreement with histopathology (kappa=0.692). No significant difference was found in withdrawal time (29 min vs. 27 min; p>0.05), indicating that AI did not delay the procedure.

Conclusions: Although AI did not significantly increase ADR compared to conventional colonoscopy, it demonstrated strong histopathological concordance, supporting its reliability in lesion characterization. AI may reduce interobserver variability and optimize real-time decision-making, reinforcing its clinical utility in CRC screening.