Scientific Meeting

European Association for Endoscopic Surgery

Maastricht 2024

During the international congress EAES 2024 – European Association for Endoscopic Surgery, held in Maastricht, Prof. Daniela Pacella presented and explored the main themes of her research. Participation in the event provided an important opportunity for dialogue with the international scientific community, fostering the exchange of experiences, knowledge, and development perspectives. The images capture some significant moments, bearing witness to the ongoing commitment to advancing our research

World Society of Emergency Surgery Congress

Rodi 2024

At the international WSES 2024 congress in Rhodes, Prof. Mauro Podda presented the key phases and objectives of the MINERVA project, highlighting its impact and future perspectives. Participation in the event facilitated meaningful exchanges with international colleagues and researchers, strengthening scientific collaborations and promoting the dissemination of our research findings

National Conference of the Italian Society of Medical Statistics and Clinical Epidemiology (SISMEC)

Pavia 9-12 September 2025

The MINERVA project was presented at the National Conference of the Italian Society of Medical Statistics and Clinical Epidemiology (SISMEC), held from September 9–12 at the University of Pavia.

The presentation, delivered on September 11 by Adriano De Simone and Prof. Dario Bruzzese, showcased the project’s results to the scientific community, highlighting its contribution to the field of medical statistics and clinical research.

XLIX Congresso AIE

Salerno 2025

At the 49th Congress of the Italian Association of Epidemiology (Salerno, April 8–11, 2025), Prof. Daniela Pacello (University of Naples Federico II) presented the MINERVA project during her talk titled:

“MINERVA: the first multicenter prospective study to predict relapse of biliary acute pancreatitis”.

MINERVA is the first ongoing multicenter prospective study aimed at developing predictive models for the relapse risk of biliary acute pancreatitis, combining clinical data with machine learning approaches.