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Application of Robot-assisted Pancreaticobiliary Junction Resection in Benign Duodenal Tumors
Published on: December 20, 2024
Fully Automated Detection of Pancreaticobiliary Maljunction Based on 3D Magnetic Resonance Cholangiopancreatography
Xiaoyu Zhang1, Weizheng Liu1,2, Ziyue Chang1
1Faculty of Hepato-Pancreato-Biliary Surgery, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Introduction:
Pancreaticobiliary maljunction (PBM) is closely related to biliary tract cancer. The early detection of PBM is crucial but challenging. We aimed 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 = 0.01) and biliary tract cancer (11% vs. 3%, p = 0.03) than controls. The optimal model achieved a mean absolute error 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.