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REVIEW article

Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1487271
This article is part of the Research Topic Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume V View all 12 articles

Advances in the clinical application of machine learning in acute pancreatitis: a review

Provisionally accepted
Zhaowang Tan Zhaowang Tan Gaoxiang Li Gaoxiang Li Yueliang Zheng Yueliang Zheng Qian Li Qian Li Wenwei Cai Wenwei Cai Jianfeng Tu Jianfeng Tu Senjun Jin Senjun Jin *
  • Zhejiang Provincial People's Hospital, Hangzhou, China

The final, formatted version of the article will be published soon.

    Traditional disease prediction models and scoring systems for acute pancreatitis (AP) are often inadequate in providing concise, reliable, and effective predictions regarding disease progression and prognosis. As a novel interdisciplinary field within Artificial Intelligence (AI), machine learning (ML) is increasingly being applied to various aspects of AP, including severity assessment, complications, recurrence rates, organ dysfunction, and the timing of surgical intervention. This review focuses on recent advancements in the application of ML models in the context of AP.

    Keywords: artificial intelligence, Machine-learning model, acute pancreatitis, severity, complications, Recurrence, Mortality

    Received: 27 Aug 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 Tan, Li, Zheng, Li, Cai, Tu and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Senjun Jin, Zhejiang Provincial People's Hospital, Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.