Accurate measurement of key structures in CBD patients using deep learning

Abstract

The prevalence of common bile duct (CBD) stones presents a widespread and intricate clinical challenge that necessitates precise measurements for effective clinical management. In response, we developed a practical deep learning system designed to precisely measure key structures within endoscopic retrograde cholangiopancreatography (ERCP) images in CBD patients. This system excels in detecting key structures in CBD patients and provides comprehensive information about the quantity, size, location, and dimensions of the common bile duct. While the size, location, and dimensions of the CBD are derived from the scaling ratio of the estimated duodenoscope diameter, the quantity of stones is determined independently of this conversion between pixels and millimeters. A rigorous evaluation of the system’s performance was conducted utilizing diverse metrics such as the Dice coefficient, accuracy, area under the receiver operating characteristic (AUROC) curve, and a comprehensive confusion matrix. A dataset of 418 images from 403 patients was divided into three sets: 314 images (75%) from 299 patients (74%) for training, 52 images (12.5%) from 52 patients (13%) for validation, and 52 images (12.5%) from 52 patients (13%) for testing. The proposed system demonstrated remarkable results in key structure detection and measurement, with Dice coefficients and overall AUROCs of 95.6% and 95.0% for the duodenoscope, 94.6% and 94.0% for the bile duct, and 94.1% and 92.0% for stone identification, respectively. The system’s timely, precise measurements of CBD structures can significantly enhance clinical decision-making, improve patient outcomes, and streamline diagnostic and management processes.