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

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

Abstract 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 thes...

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Main Authors: Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella
Format: Article
Language:English
Published: BMC 2025-03-01
Series:World Journal of Emergency Surgery
Subjects:
Acute biliary pancreatitis
Recurrence
Hospital readmission
Cholecystectomy
Machine learning
Artificial intelligence
Online Access:https://doi.org/10.1186/s13017-025-00594-7

Internet

https://doi.org/10.1186/s13017-025-00594-7

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