AI and Polyp Detection During Colonoscopy
Marco Spadaccini, Maddalena Menini, Davide Massimi, Tommy Rizkala, Roberto de Sire, Ludovico Alfarone, Antonio Capogreco, Matteo Colombo, Roberta Maselli, Alessandro Fugazza, Luca Brandaleone, Antonio Di Martino, Daryl Ramai, Alessandro Repici, Cesare Hassan
Abstract
Colorectal cancer (CRC) prevention depends on effective colonoscopy; yet variability in adenoma detection rates (ADRs) and missed lesions remain significant hurdles. Artificial intelligence-powered computer-aided detection (CADe) systems offer promising advancements in enhancing polyp detection. This review examines the role of CADe in improving ADR and reducing adenoma miss rates (AMRs) while addressing its broader clinical implications. CADe has demonstrated consistent improvements in ADRs and AMRs; largely by detecting diminutive polyps, but shows limited efficacy in identifying advanced adenomas or sessile serrated lesions. Challenges such as operator deskilling and the need for enhanced algorithms persist. Combining CADe with adjunctive techniques has shown potential for further optimizing performance. While CADe has standardized detection quality; its long-term impact on CRC incidence and mortality remains inconclusive. Future research should focus on refining CADe technology and assessing its effectiveness in reducing the global burden of CRC.