LAUSR

BACKGROUNDS AND AIMS Magnetic resonance cholangiopancreatography (MRCP) can accurately diagnose common bile duct (CBD) stones, but is laborious to interpret. We developed an artificial neural network (ANN) capable of automatically assisting physicians with the diagnosis of CBD stones. This study aimed to evaluate the ANN's diagnostic performance for detecting CBD stones in thick-slab MRCP images and identify clinical factors predictive of accurate diagnosis. METHODS The presence of CBD stones was confirmed via direct visualization through endoscopic retrograde cholangiopancreatography (ERCP). The absence of CBD stones was confirmed by either a negative endoscopic ultrasound accompanied by clinical improvements or negative findings on ERCP. Our base networks were constructed using state-of-the-art EfficientNet-B5 neural network models, which are widely used for image classification. RESULTS In total, 3156 images were collected from 789 patients. Of these, 2628 images from 657 patients were used for training. An additional 1924 images from 481 patients were prospectively collected for validation. Across the entire prospective validation cohort, the ANN achieved a sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and overall accuracy of 93.03%, 97.05%, 97.01%, 93.12%, and 95.01%, respectively. Similarly, a radiologist achieved a sensitivity, specificity, PPV, NPV, and overall accuracy 91.16%, 93.25%, 93.22%, 90.20%, and 91.68%, respectively. In multivariate analysis, only bile duct diameter >10mm (OR=2.45, 95% CI [1.08-6.07], P=0.040) was related to ANN diagnostic accuracy. CONCLUSION Our ANN algorithm automatically and quickly diagnoses CBD stones in thick-slab MRCP images, therein aiding physicians with optimizing clinical practice, such as whether to perform ERCP.