Chun Angela, Bautista-Castillo Abraham, Osuna Isabella, Nasto Kristiana, Munoz Flor M, Schutze Gordon E, Devaraj Sridevi, Muscal Eyal, de Guzman Marietta M, Sexson Tejtel Kristen, Vogel Tiphanie P, Kakadiaris Ioannis A
Division of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital; Houston, TX 77030, USA.
Computational Biomedicine Lab, Department of Computer Science, University of Houston; Houston, TX 77204, USA.
J Infect Dis. 2025 Jan 7;231(4):931-9. doi: 10.1093/infdis/jiaf004.
The pandemic emergent disease multisystem inflammatory syndrome in children (MIS-C) following coronavirus disease-19 infection can mimic endemic typhus. We aimed to use artificial intelligence (AI) to develop a clinical decision support system that accurately distinguishes MIS-C versus Endemic Typhus (MET).
Demographic, clinical, and laboratory features rapidly available following presentation were extracted for 133 patients with MIS-C and 87 patients hospitalized due to typhus. An attention module assigned importance to inputs used to create the two-phase AI-MET. Phase 1 uses 17 features to arrive at a classification manually (MET-17). If the confidence level is not surpassed, 13 additional features are added to calculate MET-30 using a recurrent neural network.
While 24 of 30 features differed statistically, the values overlapped sufficiently that the features were clinically irrelevant distinguishers as individual parameters. However, AI-MET successfully classified typhus and MIS-C with 100% accuracy. A validation cohort of 111 additional patients with MIS-C was classified with 99% accuracy.
Artificial intelligence can successfully distinguish MIS-C from typhus using rapidly available features. This decision support system will be a valuable tool for front-line providers facing the difficulty of diagnosing a febrile child in endemic areas.
新型冠状病毒肺炎（COVID-19）感染后出现的儿童多系统炎症综合征（MIS-C）可类似地方性斑疹伤寒。我们旨在利用人工智能（AI）开发一种临床决策支持系统，以准确区分MIS-C和地方性斑疹伤寒（MET）。
提取了133例MIS-C患者和87例因斑疹伤寒住院患者就诊后迅速可得的人口统计学、临床和实验室特征。一个注意力模块对用于创建两阶段AI-MET的输入赋予重要性。第1阶段使用17个特征手动得出分类结果（MET-17）。如果置信水平未被超越，则添加另外13个特征，使用递归神经网络计算MET-30。
虽然30个特征中的24个在统计学上存在差异，但这些值的重叠程度足以使这些特征作为单独参数在临床上成为无关紧要的区分因素。然而，AI-MET成功地以100%的准确率对斑疹伤寒和MIS-C进行了分类。另外111例MIS-C患者的验证队列分类准确率为99%。
人工智能可以利用迅速可得的特征成功区分MIS-C和斑疹伤寒。对于在地方性流行地区面临诊断发热儿童困难的一线医疗人员而言，该决策支持系统将是一个有价值的工具。
