Trial reportEndoscopy2023
Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model.
Trial report in Endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep Learning-Based Computer-Aided Detection and Diagnosis System for Malignant Biliary Stricture (With Video).Cancers · 2026Article
- Advances in Diagnosis and Treatment With Cholangiopancreatoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Evolving techniques to optimize ERCP-based sampling and evaluation of malignant biliary strictures.Gastroenterology report · 2026Review
- Vendor-Agnostic Vision Transformer-Based Artificial Intelligence for Peroral Cholangioscopy: Diagnostic Performance in Biliary Strictures Compared With Convolutional Neural Networks and Endoscopists.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Article
- European Consensus Recommendations for Direct Cholangioscopy and Pancreatoscopy Using a Modified Delphi Process.United European gastroenterology journal · 2025Article
- Artificial Intelligence in Pancreatobiliary Endoscopy: Current Advances, Opportunities, and Challenges.Journal of clinical medicine · 2025Review
- Peroral cholangioscopy: past, present and future.Clinical endoscopy · 2025Review
- Diagnosis of Cholangiocarcinoma: The New Biological and Technological Horizons.Diagnostics (Basel, Switzerland) · 2025Review
- Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease.Cancers · 2025Review
- Diagnostic Approach to Biliary Strictures.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology.Journal of clinical medicine · 2025Article
- Through-the-cholangioscope metal biliary stent placement as a novel endoscopic technique for bile duct strictures.Endoscopy · 2024Article
- Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures.Endoscopy international open · 2024Article
- [A look into the neighboring discipline: eHealth in oncology].Chirurgie (Heidelberg, Germany) · 2024Review
- Article
- The Role of Cholangioscopy in Biliary Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Usefulness of artificial intelligence-assisted digital single-operator cholangioscopy as a second-opinion consultation tool during interhospital assessment of an indeterminate biliary stricture: a case report.VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy · 2023Article
- [The Era of Intelligent Endoscopy: How Artificial Intelligence Empowers Digestive Endoscopy].Acta gastroenterologica LatinoamericanaReview
Corrections and comments
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and to clinically validate the model through comparisons with DSOC expert and nonexpert endoscopists.
methodsIn this two-stage study, we first developed and validated CNN1. Then, we performed a multicenter diagnostic trial to compare four DSOC experts and nonexperts against an improved model (CNN2). Lesions were classified into neoplastic and non-neoplastic in accordance with Carlos Robles-Medranda (CRM) and Mendoza disaggregated criteria. The final diagnosis of neoplasia was based on histopathology and 12-month follow-up outcomes.
resultsIn stage I, CNN2 achieved a mean average precision of 0.88, an intersection over the union value of 83.24 %, and a total loss of 0.0975. For clinical validation, a total of 170 videos from newly included patients were analyzed with the CNN2. Half of cases (50 %) had neoplastic lesions. This model achieved significant accuracy values for neoplastic diagnosis, with a 90.5 % sensitivity, 68.2 % specificity, and 74.0 % and 87.8 % positive and negative predictive values, respectively. The CNN2 model outperformed nonexpert #2 (area under the receiver operating characteristic curve [AUC]-CRM 0.657 vs. AUC-CNN2 0.794,
conclusionsThe proposed CNN model distinguished neoplastic bile duct lesions with good accuracy and outperformed two nonexpert and one expert endoscopist.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.