Advances in the clinical application of machine learning in acute pancreatitis: a review
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 (...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1487271/full |
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| _version_ | 1841556758032547840 |
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| author | Zhaowang Tan Gaoxiang Li Yueliang Zheng Qian Li Wenwei Cai Jianfeng Tu Senjun Jin |
| author_facet | Zhaowang Tan Gaoxiang Li Yueliang Zheng Qian Li Wenwei Cai Jianfeng Tu Senjun Jin |
| author_sort | Zhaowang Tan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eaebd8aa2e93458b9cf90b9356ad96b8 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-eaebd8aa2e93458b9cf90b9356ad96b82025-01-07T06:42:36ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011110.3389/fmed.2024.14872711487271Advances in the clinical application of machine learning in acute pancreatitis: a reviewZhaowang TanGaoxiang LiYueliang ZhengQian LiWenwei CaiJianfeng TuSenjun JinTraditional 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.https://www.frontiersin.org/articles/10.3389/fmed.2024.1487271/fullartificial intelligencemachine-learning modelacute pancreatitisseveritycomplicationsrecurrence |
| spellingShingle | Zhaowang Tan Gaoxiang Li Yueliang Zheng Qian Li Wenwei Cai Jianfeng Tu Senjun Jin Advances in the clinical application of machine learning in acute pancreatitis: a review Frontiers in Medicine artificial intelligence machine-learning model acute pancreatitis severity complications recurrence |
| title | Advances in the clinical application of machine learning in acute pancreatitis: a review |
| title_full | Advances in the clinical application of machine learning in acute pancreatitis: a review |
| title_fullStr | Advances in the clinical application of machine learning in acute pancreatitis: a review |
| title_full_unstemmed | Advances in the clinical application of machine learning in acute pancreatitis: a review |
| title_short | Advances in the clinical application of machine learning in acute pancreatitis: a review |
| title_sort | advances in the clinical application of machine learning in acute pancreatitis a review |
| topic | artificial intelligence machine-learning model acute pancreatitis severity complications recurrence |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1487271/full |
| work_keys_str_mv | AT zhaowangtan advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview AT gaoxiangli advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview AT yueliangzheng advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview AT qianli advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview AT wenweicai advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview AT jianfengtu advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview AT senjunjin advancesintheclinicalapplicationofmachinelearninginacutepancreatitisareview |