Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
PMID: 39556821

Abstract

BACKGROUND: Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD.

Authors

  • Jinpu Zhu
    College of Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China.
  • Fushuang Yang
    Center of Children's Clinic, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Zhongtian Wang
    College of Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China.
  • Yao Xiao
    School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China; Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Lie Wang
    School of Fine Arts and Design Department of Product Design, Guangzhou University, Guangzhou 510006, Guangdong, China.
  • Liping Sun
    School of Medical Informatics Engineering, Anhui University of Chinese Medicine, Hefei, Anhui 230012, China.

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