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开发和验证一个自动分类器来诊断儿童中耳炎的诊断

Nader Shaikh¹, Shannon J Conway¹, Jelena Kovacevic²

¹Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pennsylvania.

JAMA pediatrics

|March 4, 2024

概括

一个人工智能工具准确地诊断儿童的急性中耳炎 (AOM),使用智能手机的鼓膜视频. 这种人工智能决策支持系统显示出高灵敏度和特异性,有助于初级保健诊断和治疗决策.

科学领域:

儿科医学儿科医学
医疗保健中的人工智能
耳鼻喉科耳鼻喉科耳鼻喉科

背景情况:

急性中耳炎 (AOM) 是一种常见的儿童疾病,其诊断准确性在历史上较低.
现有的神经网络用于AOM检测的应用已经显示出有限的临床实用性.

研究的目的:

开发和验证人工智能决策支持工具,用于解释耳膜视频.
为了提高儿童患者诊断AOM的准确性.

主要方法:

一项诊断研究分析了来自门诊儿童的智能手机捕捉的耳视镜视频.
在注释的耳膜视频上训练了一个深度残留循环神经网络,以预测AOM.
人工智能的诊断准确度与决策树模型进行了比较,并包含了一个噪音质量过器.

主要成果:

深度残留循环神经网络实现了AOM与无AOM的高诊断准确性 (93.8%的灵敏度,93.5%的特异性).
人工智能模型的表现与决策树方法可比.
tympanic 膜的凸起是AOM预测最强的指标 (在确定的病例中准确率为100%).

结论:

开发的人工智能算法在从耳视镜视频中诊断AOM方面表现出高准确度.
人工智能工具凭借其图像采集和质量过功能,适用于初级和急性护理环境.
这项技术可以帮助临床医生进行自动化的AOM诊断和治疗规划.

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