Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning

Abstract PURPOSE: This study aims to detect common bile duct (CBD) dilatation using deep learning methods from artificial intelligence algorithms. METHODS: To create a convolutional neural network (CNN) model, 77 magnetic resonance cholangiopancreatography (MRCP) images without CBD dilatation and 70 MRCP images with CBD dilatation were used. The system was developed using coronal maximum intensity projection reformatted 3D-MRCP images. The ResNet50, DenseNet121, and visual geometry group models were selected for training, and detailed training was performed on each model. RESULTS: