Machine learnINg for the rElapse Risk eValuation in Acute Biliary Pancreatitis.

Plain-language summary

The MINERVA (Machine learnINg for the rElapse Risk eValuation in Acute biliary pancreatitis) project stems from the need in the clinical practice of taking an operational decision in patients that are admitted to the hospital with a diagnosis of acute biliary pancreatitis. In particular, the MINERVA prospective cohort study aims to develop a predictive score that allows to assess the risk of hospital readmission for patients diagnosed with mild biliary acute pancreatitis using Machine Learning and artificial intelligence. The objectives of the MINERVA study are to: 1. Propose a novel methodology for the assessment of the risk of relapse in patients with mild biliary acute pancreatitis who did not undergo early cholecystectomy (within 3 to 7 days from hospital admission); 2. Propose a Machine Learning predictive model using a Deep Learning architecture applied to easily collectable data; 3. Validate the MINERVA score on an extensive, multicentric, prospective cohort; 4. Allow national and international clinicians, medical staff, researchers and the general audience to freely and easily access the MINERVA score computation and use it in their daily clinical practice. The MINERVA score model will be developed on a retrospective cohort of patients (MANCTRA-1, already registered in ClinicalTrials.gov) and will be validated on a novel prospective multicentric cohort. After validation, the MINERVA score will be free and easy to compute instantly for all medical staff; it will be accessible at any time on the MINERVA website and web app, and will provide an immediate and reliable result that can be a clear indication for the best treatment pathway for the clinician and for the patient.

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Trial details