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Progress in the application of machine learning in CT diagnosis of acute appendicitis.

Acute appendicitis represents a prevalent condition within the spectrum of acute abdominal pathologies, exhibiting a diverse clinical presentation. Computed tomography (CT) imaging has emerged as a prospective diagnostic modality for the identification and differentiation of appendicitis. This revie...

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Published in:Abdominal Radiology Vol. 50; no. 9; pp. 4040 - 4050
Main Authors: LI, Jiaxin, Ye, Jiayin, Luo, Yiyun, Xu, Tianyang, Jia, Zhenyi
Format: Journal Article
Published: Springer Nature Sep2025
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: Progress in the application of machine learning in CT diagnosis of acute appendicitis.
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          LI, Jiaxin
          Ye, Jiayin
          Luo, Yiyun
          Xu, Tianyang
          Jia, Zhenyi
        affil: https://ror.org/0220qvk04 Shanghai Jiao Tong University, Shanghai, China
      sug:
      ab: Acute appendicitis represents a prevalent condition within the spectrum of acute abdominal pathologies, exhibiting a diverse clinical presentation. Computed tomography (CT) imaging has emerged as a prospective diagnostic modality for the identification and differentiation of appendicitis. This review aims to synthesize current applications, progress, and challenges in integrating machine learning (ML) with CT for diagnosing acute appendicitis while exploring prospects. ML-driven advancements include automated detection, differential diagnosis, and severity stratification. For instance, deep learning models such as AppendiXNet achieved an AUC of 0.81 for appendicitis detection, while 3D convolutional neural networks (CNNs) demonstrated superior performance, with AUCs up to 0.95 and an accuracy of 91.5%. ML algorithms effectively differentiate appendicitis from similar conditions like diverticulitis, achieving AUCs between 0.951 and 0.972. They demonstrate remarkable proficiency in distinguishing between complex and straightforward cases through the innovative use of radiomics and hybrid models, achieving AUCs ranging from 0.80 to 0.96. Even with these advancements, challenges remain, such as the "black-box" nature of artificial intelligence, its integration into clinical workflows, and the significant resources required. Future directions emphasize interpretable models, multimodal data fusion, and cost-effective decision-support systems. By addressing these barriers, ML holds promise for refining diagnostic precision, optimizing treatment pathways, and reducing healthcare costs.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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