Efficacy of an artificial neural network algorithm based on thick‐slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones

• Published in: Journal of gastroenterology and hepatology Vol. 36; no. 12; pp. 3532 - 3540
• Main Authors: Hou, Jong‐Uk, Park, Se Woo, Park, Seon Mee, Park, Da Hae, Park, Chan Hyuk, Min, Seonjeong
• Format: Journal Article
• Language: English
• Published: Australia Wiley Subscription Services, Inc 01.12.2021
• Subjects: Accuracy; Algorithms; Artificial; Bile duct; Bile ducts; Cholangiopancreatography, Magnetic Resonance; Common Bile Duct - diagnostic imaging; Diagnosis; Endoscopy; Humans; Magnetic resonance cholangiopancreatography (MRCP); Magnetic resonance imaging; Multivariate analysis; Neural network; Neural networks; Neural Networks, Computer; Patients; Reproducibility of Results; Stone; Artificial; Neural network; Magnetic resonance cholangiopancreatography (MRCP); Stone; Bile duct
• ISSN: 0815-9319; 1440-1746; 1440-1746
• DOI: 10.1111/jgh.15569

Background and Aim 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, negative predictive value, and overall accuracy of 93.03%, 97.05%, 97.01%, 93.12%, and 95.01%, respectively. Similarly, a radiologist achieved a sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy 91.16%, 93.25%, 93.22%, 90.20%, and 91.68%, respectively. In multivariate analysis, only bile duct diameter > 10 mm (odds ratio = 2.45, 95% confidence interval [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.