Artificial Intelligence‐Based Prediction of Invasion Depth in Colorectal Cancer via Endoscopic Imaging (With Video): A Narrative Review

Background and Aim Endoscopic prediction of colorectal cancer (CRC) invasion depth is essential for determining optimal treatment strategy. Artificial intelligence (AI) may assist in distinguishing between superficial (Tis/T1a) and deeply invasive (T1b) lesions to avoid unnecessary surgery. In this narrative review, we summarize recent advances in this field. Methods A database search of PubMed, Scopus, Cochrane Library, and ClinicalTrials.gov was conducted in September 2025 to identify original peer‐reviewed studies that developed or validated AI‐based models using endoscopic imaging for invasion‐depth prediction. Studies reporting diagnostic metrics such as sensitivity, specificity, accuracy, and AUC were included. Results Ten studies met the inclusion criteria, categorized into three groups: (1) AI prediction using image‐enhanced endoscopy, (2) AI prediction using white‐light imaging, and (3) AI prediction using multi‐modal data (imaging with clinical information). The latest models achieved high performance for T1b CRC diagnosis (e.g., AUC 0.851), with some demonstrating performance comparable to expert endoscopists. However, a pooled analysis was not performed due to dataset heterogeneity and limited sample sizes. Conclusions AI‐assisted systems show promise for improving the prediction of invasion depth in CRC and supporting real‐time decision‐making. However, limited sample sizes for training and test datasets and an imbalance in the training dataset remain key challenges. Large‐scale, multicenter validation studies and the development of open‐access databases are essential for clinical implementation.