Artificial intelligence-empowered assessment of bile duct stone removal challenges

The aim of this investigation was to unravel the complexities inherent in endoscopic retrograde cholangiopancreatography (ERCP) procedures for bile duct stone removal by leveraging advanced artificial intelligence (AI) methodologies to support clinical decision-making and optimize patient outcomes. We introduced the Assessment of Stone Removal Challenges (ACRS) system, a novel integration of Data-efficient Image Transformers (DeiT) for image data analysis and eXtreme Gradient Boosting (XGBoost) for clinical data interpretation. Our study included a patient cohort of 2,129 individuals, focusing on training the ACRS system to achieve high diagnostic precision. Using logistic regression, we identified pivotal predictors affecting the complexity of bile duct stone removal. These findings were visually represented through forest plots and nomograms. Analytical and visualization processes were conducted using the Python and R programming languages, adhering to a p value significance threshold of less than 0.05. By utilizing DeiT enhanced by transfer learning and XGBoost for clinical data interpretation, the system achieved an accuracy of 0.83 and a perfect recall rate on a test set of 2,129 patients. Gradient-weighted class activation mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) provided in-depth diagnostic insights. Logistic regression was applied to identify crucial clinical predictors for stone removal difficulty, as visualized through forest plots and nomograms. These tools facilitated measurable assessments of procedural complexity, making significant strides in gastroenterology diagnostics and decisionmaking. By adopting causal inference techniques, the system effectively quantifies the influence of various clinical entities on stone removal difficulty, thereby augmenting both diagnostic precision and procedural strategies in ERCP.