Congenital biliary dilatation necessitates timely intervention owing to potential complications. This study endeavors to enhance diagnostic precision using quantitative three-dimensional morphological characteristics. Objectives involve developing models to differentiate congenital from secondary biliary dilatation and identify intrahepatic involvement. Employing machine learning, robust diagnostic models aim to elevate clinical detection rates and improve accuracy.
Sex
ALL
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Area under the Receiver Operating Characteristic curve (AUROC) of the diagnostic models for the differential diagnosis of congenital and secondary biliary dilatation and the identification of intrahepatic involvement
Timeframe: Pre-operation