ArticleCancers2023
Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep Learning-Based Computer-Aided Detection and Diagnosis System for Malignant Biliary Stricture (With Video).Cancers · 2026Article
- Clinical Validation of AI-Assisted Evaluation of Indeterminate Biliary Strictures in Digital-Single Operator Cholangioscopy: A Transcontinental Multicentric Study.Clinical and translational gastroenterology · 2026Article
- Advances in Diagnosis and Treatment With Cholangiopancreatoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Evaluation and management of primary sclerosing cholangitis patients awaiting liver transplantation.JHEP reports : innovation in hepatology · 2026Review
- Artificial Intelligence in Pancreatobiliary Endoscopy: Current Advances, Opportunities, and Challenges.Journal of clinical medicine · 2025Review
- Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease.Cancers · 2025Review
- Artificial intelligence for automatic diagnosis and pleomorphic morphological characterization of malignant biliary strictures using digital cholangioscopy.Scientific reports · 2025Article
- Review
- Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.Journal of clinical medicine · 2025Review
- Applications of artificial intelligence in biliary tract cancers.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Review
- Article
- Artificial Intelligence in Cholangioscopy: A New Frontier for Indeterminate Biliary Strictures.Euroasian journal of hepato-gastroenterologyReview
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.
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