Mapping malignancy: Multicenter study addressing topographic challenges in biliary stricture artificial intelligence analysis

Background and study aims: Cholangiocarcinoma (CCa) is a complex malignancy of the biliary tract, classified as intrahepatic, perihilar, or distal. Digital single-operator cholangioscopy (D-SOC) enhances evaluation of biliary strictures, although it remains limited by suboptimal biopsy yield and technical constraints. Artificial intelligence (AI), particularly convolutional neural networks (CNNs), has emerged as a promising adjunct. However, performance across anatomical subtypes is not well defined. This study evaluated diagnostic performance of an AI-based model in detecting CCa lesions by location. Pattients and methods: A YOLOv8-based CNN was trained and validated using 315,993 D-SOC images from 183 patients across six international high-volume centers. Images were labeled as benign or malignant based on expert consensus. Frame-based analysis assessed diagnostic performance using macro-average F1-score, precision, and recall. Subgroup analysis explored anatomical site-specific performance. Results: Among the included patients (mean age 66.1±12.1 years; 64.5% male), 43.2% had perihilar, 37.2% intrahepatic, and 19.7% distal biliary strictures. The model demonstrated high overall performance: F1 score 95.3%, precision 95.5%, and recall 95.1%. Site-specific analysis revealed an F1 score of 89.6% for distal strictures and 91.1% for perihilar strictures. Receiver operating characteristic curves showed areas under the curve of 0.980 for intrahepatic strictures and 0.990 for perihilar and distal strictures. Conclusions: This is the first study to demonstrate AI-based diagnostic performance across CCa topography using a large, multicenter D-SOC dataset. Although anatomical complexity affects detection, the model’s high precision and generalizability suggest potential for clinical utility. These results support application of an AI-enhanced algorithm decision algorithm for cholangioscopy.