基于医生的诊断与诊断性人工智能算法在墨西哥检测传染性发热疾病中的有效性。
Effectiveness of physician-based diagnosis versus diagnostic artificial intelligence algorithms in detecting communicable febrile diseases in Mexico.
作者信息
Medina Fuentes Enrique Alonso, Ruíz Valdez Carmen Alicia, Hernández Bautista Porfirio Felipe, Cabrera Gaytán David Alejandro, Olivas Fabela Guadalupe Minerva, Mireles Garza José Alberto, Alejo Martínez Olga María, Rocha Reyes Brenda Leticia, Vallejos Parás Alfonso, Arriaga Nieto Lumumba, Pérez Andrade Yadira, Jaimes Betancourt Leticia, Valle Alvarado Gabriel, Cruz Orozco Oscar, Rivera Mahey Mónica Grisel
机构信息
Hospital General Regional No. 1 Obregón, Instituto Mexicano del Seguro Social, Sonora, México.
Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Mexico City, México.
出版信息
Digit Health. 2025 Jun 30;11:20552076251353292. doi: 10.1177/20552076251353292. eCollection 2025 Jan-Dec.
BACKGROUND
Digital medicine is an important tool in the current healthcare landscape. Fever is an important reason for evaluating patients at first and second levels of care and a frequent symptom of diseases subject to epidemiological surveillance.
OBJECTIVE
To evaluate the diagnostic effectiveness of various algorithms in detecting communicable diseases of epidemiological interest in febrile patients at Hospital General Regional No. 1, Cd. Obregón, Sonora.
METHODS
An observational, descriptive, and retrospective study was conducted in a second-level hospital from 1 January 2022 to 31 December 2023, to determine Cohen's kappa and the sensitivity, specificity, positive and negative predictive values, precision and Youden's J index of diagnostic algorithms for 20 communicable diseases with respect to the doctors' diagnoses.
RESULTS
Diagnostic algorithms were applied to the data of 909 cases. The sensitivities of Mediktor®, an artificial neural network-based algorithm, a medical diagnostic algorithm and a composite diagnostic algorithm were 11.97%, 64.09%, 69.92% and 99.37%, respectively, and the corresponding specificities were 93.43%, 91.24%, 27.01% and 5.11%, respectively. The neural network-based method yielded the highest Youden's J index.
CONCLUSIONS
The medical diagnostic algorithm had the best sensitivity, whereas the specificity was greater for the two artificial intelligence algorithms.
背景
数字医学是当前医疗保健领域的一项重要工具。发热是一级和二级医疗保健机构评估患者的重要原因，也是受流行病学监测疾病的常见症状。
目的
评估各种算法在检测索诺拉州奥布雷贡市第一综合区域医院发热患者中具有流行病学意义的传染病方面的诊断效果。
方法
于2022年1月1日至2023年12月31日在一家二级医院开展了一项观察性、描述性和回顾性研究，以确定20种传染病诊断算法相对于医生诊断的科恩kappa系数、敏感性、特异性、阳性和阴性预测值、精确度及约登指数。
结果
对909例病例的数据应用了诊断算法。基于人工神经网络的算法Mediktor®、一种医学诊断算法和一种复合诊断算法的敏感性分别为11.97%、64.09%、69.92%和99.37%，相应的特异性分别为93.43%、91.24%、27.01%和5.11%。基于神经网络的方法得出了最高的约登指数。
结论
医学诊断算法具有最佳的敏感性，而两种人工智能算法的特异性更高。
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