Fully automated detection of pancreaticobiliary maljunction based on 3D magnetic resonance cholangiopancreatographyusing deep learning

INTRODUCTION: Pancreaticobiliary maljunction (PBM) is closely related to biliary tract cancer. The early detection of PBM is crucial but challenging. We aim to develop and validate a deep learning model for fully automatically detection of PBM using magnetic resonance cholangiopancreatography (MRCP) images. METHODS: Clinical and imaging data from patients who underwent MRCP examinations from January 2020 to December 2021 were retrospectively collected. A total of 200 patients (100 with confirmed PBM and 100 non-PBM controls) were enrolled. The dataset was randomly divided into training, validation, and test sets in a 6:2:2 ratio. The training and validation sets were used to train YOLOv5 and Inception-ResNetV2. The test set was used to evaluate the accuracy of the model. RESULTS: The PBM group had a higher proportion of females (62% vs. 42%, P = .01) and biliary tract cancer (11% vs. 3%, P = .03) than controls. The optimal model achieved a mean absolute error (MAE) of 6.2% and an F1 score of 93.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 95.0%, 92.5%, 92.7%, and 94.9%, respectively. CONCLUSION: The proposed deep learning model demonstrates high accuracy in automated PBM detection on MRCP images, offering potential to improve diagnostic efficiency and reduce interobserver variability.