Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms

Predictive models in EUS/ERCP

Predictive models (PMs) in endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) have the potential to improve patient outcomes, enhance diagnostic accuracy, and guide therapeutic interventions. This review aims to summarize the current state of predictive models in ERCP and EUS and their clinical implications. To be considered useful in clinical practice a PM should be accurate, easy to perform, and may consider objective variables. PMs in ERCP estimate correct indication, probability of success, and the risk of developing adverse events. These models incorporate patient-related factors and technical aspects of the procedure. In the field of EUS, these models utilize clinical and imaging data to predict the likelihood of malignancy, presence of specific lesions, or risk of complications related to therapeutic interventions. Further research, validation, and refinement are necessary to maximize the utility and impact of these models in routine clinical practice.