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  1. Home
  3. Mapping Malignancy: Multicenter Study Addressing Topographic Challenges In Biliary Stricture Artificial Intelligence Analysis.
  1. Home
  3. Mapping Malignancy: Multicenter Study Addressing Topographic Challenges In Biliary Stricture Artificial Intelligence Analysis.

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Related Experiment Video

Intraoperative Strategy under Complex Vascular Adhesion for Laparoscopic Radical Resection of Bismuth-Corlette Type IIIb Perihilar Cholangiocarcinoma
05:22

Intraoperative Strategy under Complex Vascular Adhesion for Laparoscopic Radical Resection of Bismuth-Corlette Type IIIb Perihilar Cholangiocarcinoma

Published on: February 13, 2026

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

Miguel Mascarenhas1,2, Antonio Miguel Pinto da Costa3,4, Matheus Ferreira de Carvalho4

  • 1Gastroenterology DepartmentCentro Hospitalar Universitário de São JoãoPortoPorto DistrictPortugal.

Endoscopy International Open
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

An artificial intelligence (AI) model demonstrated high accuracy in detecting cholangiocarcinoma (CCa) using digital single-operator cholangioscopy (D-SOC) images. This AI tool shows promise for improving CCa diagnosis across different biliary tract locations.

Keywords:
CholangioscopyPancreatobiliary (ERCP/PTCD)Strictures

Related Experiment Videos

Intraoperative Strategy under Complex Vascular Adhesion for Laparoscopic Radical Resection of Bismuth-Corlette Type IIIb Perihilar Cholangiocarcinoma
05:22

Intraoperative Strategy under Complex Vascular Adhesion for Laparoscopic Radical Resection of Bismuth-Corlette Type IIIb Perihilar Cholangiocarcinoma

Published on: February 13, 2026

Area of Science:

  • Gastroenterology and Hepatology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Cholangiocarcinoma (CCa) is a complex biliary tract malignancy with intrahepatic, perihilar, and distal classifications.
  • Digital single-operator cholangioscopy (D-SOC) aids biliary stricture evaluation but faces challenges with biopsy yield and technical limitations.
  • Artificial intelligence (AI), particularly convolutional neural networks (CNNs), offers a potential solution for enhancing CCa detection.

Purpose of the Study:

  • To evaluate the diagnostic performance of an AI-based model in detecting cholangiocarcinoma (CCa) lesions.
  • To assess the AI model's performance across different anatomical subtypes of CCa (intrahepatic, perihilar, distal).
  • To determine the generalizability of the AI model using a large, multicenter D-SOC image dataset.

Main Methods:

  • A YOLOv8-based convolutional neural network (CNN) was trained and validated on 315,993 D-SOC images from 183 patients across six international centers.
  • Images were classified as benign or malignant based on expert consensus.
  • Diagnostic performance was assessed using frame-based analysis, including F1-score, precision, and recall, with subgroup analysis for anatomical sites.

Main Results:

  • The AI model achieved high overall performance with an F1 score of 95.3%, precision of 95.5%, and recall of 95.1%.
  • Site-specific analysis showed F1 scores of 89.6% for distal and 91.1% for perihilar strictures.
  • Receiver operating characteristic curves indicated strong performance with areas under the curve of 0.980 for intrahepatic and 0.990 for perihilar and distal strictures.

Conclusions:

  • This study is the first to report AI-based diagnostic performance across cholangiocarcinoma (CCa) topography using a large, multicenter D-SOC dataset.
  • Despite anatomical complexities influencing detection, the AI model exhibits high precision and generalizability, suggesting significant clinical utility.
  • The findings support the integration of AI-enhanced algorithms into clinical decision-making for cholangioscopy.