Artificial Intelligence in the Diagnosis of Colonic Lesions: Advancing Beyond ConventionalColonoscopy

Abstract

Colorectal cancer (CRC) is the third most prevalent cancer in the world. Its rising incidence among young adults necessitates the development of improved early detection strategies. Although white light colonoscopy (WLC) is conventionally used as a primary screening tool, it has certain limitations, including operator dependency, inter-operator heterogeneity, Adenoma miss rates, endoscopist fatigue, and inadequate Bowel preparation. Nonetheless, histopathology of tissues remains the gold standard for diagnosis.

Artificial intelligence (AI) has emerged as a powerful tool in colonoscopic practice, as it uses deep learning techniques to enhance real-time detection and characterization of colonic lesions. Two Computer-assisted diagnostic (CAD) tools: (CADe), which improves adenoma detection rate (ADR) and reduces adenoma miss rates, and (CADx), which facilitates optical diagnosis of lesions by combining various advanced techniques and helps prevent unnecessary polypectomy and reduce healthcare costs.

Besides all these advancements in AI, there are still some challenges to overcome, such as limited real-world diversity, reduced algorithm transparency, and risk of automation bias. For achieving this multicenter validation,  standard guidelines and improved clinician training are required. Thus, integrating AI into colonoscopy marks an important shift in clinical practice, providing enhanced precision and efficiency while highlighting its role as an assistive tool in clinical expertise rather than replacing it.