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Distinguishing Multisystem Inflammatory Syndrome in Children From Typhus Using Artificial Intelligence: MIS-C Versus

Angela Chun1, Abraham Bautista-Castillo2, Isabella Osuna1

  • 1Division of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.

The Journal of Infectious Diseases
|January 6, 2025
PubMed
Summary

Artificial intelligence (AI) can now accurately differentiate multisystem inflammatory syndrome in children (MIS-C) from endemic typhus. This AI tool aids clinicians in diagnosing febrile children in endemic regions.

Keywords:
artificial intelligenceendemic typhusmachine learningmultisystem inflammatory syndrome in children (MIS-C)murine typhus

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Area of Science:

  • Pediatric infectious diseases
  • Computational medicine
  • Epidemiology

Background:

  • Multisystem inflammatory syndrome in children (MIS-C) following COVID-19 can resemble endemic typhus.
  • Accurate differentiation is crucial for appropriate patient management.

Purpose of the Study:

  • To develop and validate an AI-driven clinical decision support system (AI-MET) for distinguishing MIS-C from endemic typhus.

Main Methods:

  • Utilized demographic, clinical, and laboratory data from 133 MIS-C and 87 typhus patients.
  • Developed a two-phase AI model (MET-17 and MET-30) incorporating an attention module and recurrent neural network.
  • Employed 17 initial features, with an additional 13 features used if initial classification confidence was not met.

Main Results:

  • The AI-MET model achieved 100% accuracy in classifying typhus and MIS-C in the initial cohort.
  • A validation cohort of 111 MIS-C patients was classified with 99% accuracy.
  • Individual clinical features showed statistical differences but lacked sufficient discriminative power alone.

Conclusions:

  • AI demonstrates high efficacy in distinguishing MIS-C from typhus using readily available clinical data.
  • The AI-MET system offers a valuable tool for frontline providers managing febrile children in endemic areas.
  • This technology can improve diagnostic accuracy and timely treatment initiation.