AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

被引：0
|
作者：
Maletz, Sebastian [1 ]
Balagurunathan, Yoga [2 ]
Murphy, Kade [1 ]
Folio, Les [1 ,2 ]
Chima, Ranjit [1 ,2 ]
Zaheer, Atif [3 ]
Vadvala, Harshna [1 ,2 ]
机构：
[1] Univ S Florida, Morsani Coll Med, Tampa, FL 33620 USA
[2] H Lee Moffitt Canc Ctr & Res Inst, Tampa, FL 33607 USA
[3] Johns Hopkins Univ Hosp, Baltimore, MD USA
来源：
ABDOMINAL RADIOLOGY | 2025年 / 50卷 / 01期
关键词：
Pancreatitis imaging; Artificial intelligence (AI); Machine learning (ML); Deep learning (DL); COMPUTED-TOMOGRAPHY; RADIOMICS FEATURES; INFORMATION; CT; RECURRENCE; SEVERITY; IMAGES; PRIMER;
D O I：
10.1007/s00261-024-04512-4
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.
引用
收藏
页码：438 / 452
页数：15
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