Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol

Background Mild acute biliary pancreatitis (MABP) presents significant clinical and economic challenges due to its potential for relapse. Current guidelines advocate for early cholecystectomy (EC) during the same hospital admission to prevent recurrent acute pancreatitis (RAP). Despite these recommendations, implementation in clinical practice varies, highlighting the need for reliable and accessible predictive tools. The MINERVA study aims to develop and validate a machine learning (ML) model to predict the risk of RAP (at 30, 60, 90 days, and at 1-year) in MABP patients, enhancing decision-making processes. Methods The MINERVA study will be conducted across multiple academic and community hospitals in Italy. Adult patients with a clinical diagnosis of MABP, in accordance with the revised Atlanta Criteria, who have not undergone EC during index admission will be included. Exclusion criteria encompass non-biliary aetiology, severe pancreatitis, and the inability to provide informed consent. The study involves both retrospective data from the MANCTRA-1 study and prospective data collection. Data will be captured using REDCap. The ML model will utilise convolutional neural networks (CNN) for feature extraction and risk prediction. The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. Performance metrics such as accuracy, precision, recall, and area under the ROC curve (AUC) will be used to evaluate the model. Discussion The MINERVA study aims to address the specific gap in predicting RAP risk in MABP patients by leveraging advanced ML techniques. By incorporating a wide range of clinical and demographic variables, the MINERVA score aims to provide a reliable, cost-effective, and accessible tool for healthcare professionals. The project emphasises the practical application of AI in clinical settings, potentially reducing the incidence of RAP and associated healthcare costs. Trial registration ClinicalTrials.gov ID: NCT06124989.