Evaluation of Common Bile Duct (CBD) dilatation by CT data using Synthetic MRCP data by Cycle-GAN and 3D VGG Network

Motivation: Differentiating CBD dilatation with CT alone is challenging, often necessitating MRCP(Magnetic Resonance Cholangiopancreatography). Yet, patients forego MRCP due to cost and time constraints. Hence, predicting CBD dilatation using CT is vital for diagnosis. Goal(s): Developing deep neural networks to assess CBD dilatation only with CT data. Approach: Cycle-GAN and 3D VGG Networks predicted CBD dilatation, where Cycle-GAN generated synthetic MRCP from CT and 3D VGG Network predicted dilatation using this synthetic data. Results: The network trained with synthetic MRCP data predicted CBD dilatation with an AUROC of 0.7231, 30% improvement over using CT data alone, enabling CT-only diagnosis. Impact: This study introduces a transformative solution for CBD dilatation diagnosis, enabling assessments using Only-CT data from Cycle-GAN and 3D VGG Network. Achieving a 30% improvement in AUROC, it enables reliable CT-only diagnoses, overcoming scarce MRCP data and improve patient care.