1. Artificial intelligence for automatic diagnosis and pleomorphic morphological characterization of malignant biliary strictures using digital cholangioscopy.
- Author
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Mascarenhas M, Almeida MJ, González-Haba M, Castillo BA, Widmer J, Costa A, Fazel Y, Ribeiro T, Mendes F, Martins M, Afonso J, Cardoso P, Mota J, Fernandes J, Ferreira J, Boas FV, Pereira P, and Macedo G
- Subjects
- Humans, Constriction, Pathologic diagnosis, Constriction, Pathologic pathology, Bile Duct Neoplasms pathology, Bile Duct Neoplasms diagnosis, Endoscopy, Digestive System methods, Cholestasis diagnosis, Cholestasis pathology, Male, Female, Sensitivity and Specificity, Artificial Intelligence, Neural Networks, Computer
- Abstract
Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangioscopy (D-SOC) holds promise for improving diagnostic accuracy in indeterminate BS. This multicenter study aimed to validate a convolutional neural network (CNN) model using a large dataset of D-SOC images to automatically detect and characterize malignant BS. D-SOC exams from three centers-Centro Hospitalar Universitário de São João, Porto, Portugal (n = 123), Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain (n = 18), and New York University Langone Hospital, New York, USA (n = 23)-were included. Frames were categorized based on histopathology. The CNN's performance in detecting tumor vessels, papillary projections, nodules, and masses was assessed. The dataset was split into 90% training and 10% validation sets. Performance metrics included AUC, sensitivity, specificity, PPV, and NPV. Analysis of 96,020 images from 164 D-SOC exams (50,427 malignant strictures and 45,593 benign findings) showed the CNN achieved 92.9% accuracy, 91.7% sensitivity, 94.4% specificity, 95.1% PPV, 93.1% NPV, and an AUROC of 0.95. Accuracy rates for morphological features were 90.8% (papillary projections), 93.6% (nodules), 93.2% (masses), and 78.1% (tumor vessels). AI-driven CNN models hold promise for enhancing diagnostic accuracy in suspected biliary malignancies. This multicenter study contributes diverse datasets to ongoing research, supporting further AI applications in this patient population., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics and consent to participate declarations: The study was performed following approval from the ethics committees of Centro Hospitalar Universitário de São João/Faculdade de Medicina da Universidade do Porto (CE 41/2021), Hospital Universitario Puerta de Hierro Majadahonda (PI 153/22) and New York University Langone Hospital (IRB 03845978/2023). This was a retrospective non interventional study conducted in accordance with the Declaration of Helsinki. Therefore, there was not a need to obtain written informed consent from each subject, after evaluation by the ethics committee of each institution., (© 2025. The Author(s).)
- Published
- 2025
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