ImportanceAcute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple neural networks have been developed to recognize the presence of AOM with limited clinical application.ObjectiveTo develop and internally validate an artificial intelligence decision-support tool to interpret videos of the tympanic membrane and enhance accuracy in the diagnosis of AOM.Design, Setting, and ParticipantsThis diagnostic study analyzed otoscopic videos of the tympanic membrane captured using a smartphone during outpatient clinic visits at 2 sites in Pennsylvania between 2018 and 2023. Eligible participants included children who presented for sick visits or wellness visits.ExposureOtoscopic examination.Main Outcomes and MeasuresUsing the otoscopic videos that were annotated by validated otoscopists, a deep residual-recurrent neural network was trained to predict both features of the tympanic membrane and the diagnosis of AOM vs no AOM. The accuracy of this network was compared with a second network trained using a decision tree approach. A noise quality filter was also trained to prompt users that the video segment acquired may not be adequate for diagnostic purposes.ResultsUsing 1151 videos from 635 children (majority younger than 3 years of age), the deep residual-recurrent neural network had almost identical diagnostic accuracy as the decision tree network. The finalized deep residual-recurrent neural network algorithm classified tympanic membrane videos into AOM vs no AOM categories with a sensitivity of 93.8% (95% CI, 92.6%-95.0%) and specificity of 93.5% (95% CI, 92.8%-94.3%) and the decision tree model had a sensitivity of 93.7% (95% CI, 92.4%-94.9%) and specificity of 93.3% (92.5%-94.1%). Of the tympanic membrane features outputted, bulging of the TM most closely aligned with the predicted diagnosis; bulging was present in 230 of 230 cases (100%) in which the diagnosis was predicted to be AOM in the test set.Conclusions and RelevanceThese findings suggest that given its high accuracy, the algorithm and medical-grade application that facilitates image acquisition and quality filtering could reasonably be used in primary care or acute care settings to aid with automated diagnosis of AOM and decisions regarding treatment.

中文翻译：

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用于诊断儿童急性中耳炎的自动分类器的开发和验证


重要性急性中耳炎 (AOM) 是一种常见的儿童疾病，但诊断的准确性一直较低。已开发出多个神经网络来识别 AOM 的存在，但临床应用有限。目的开发并内部验证人工智能决策支持工具，以解释鼓膜视频并提高 AOM 诊断的准确性。设计、设置和参与者这项诊断研究分析了 2018 年至 2023 年在宾夕法尼亚州 2 个地点门诊时使用智能手机捕获的鼓膜耳镜视频。符合条件的参与者包括因病就诊或健康就诊的儿童。暴露耳镜检查。主要结果和措施使用耳镜通过经过验证的耳镜专家注释的视频，训练深度残差循环神经网络来预测鼓膜的特征以及 AOM 与无 AOM 的诊断。将该网络的准确性与使用决策树方法训练的第二个网络进行了比较。还训练了噪声质量过滤器，以提示用户获取的视频片段可能不足以用于诊断目的。结果使用来自 635 名儿童（大多数年龄小于 3 岁）的 1151 个视频，深度残差循环神经网络具有几乎相同的诊断结果决策树网络的准确性。最终的深度残差循环神经网络算法将鼓膜视频分为 AOM 与无 AOM 类别，灵敏度为 93.8%（95% CI，92.6%-95.0%），特异性为 93.5%（95% CI，92.8%-94.3） %），决策树模型的敏感性为 93.7%（95% CI，92.4%-94.9%），特异性为 93.3%（92.5%-94.1%）。 在输出的鼓膜特征中，鼓膜鼓出与预测诊断最为一致；在测试集中预测诊断为 AOM 的 230 例病例中，有 230 例 (100%) 存在凸起。结论和相关性这些研究结果表明，鉴于其高精度，算法和医疗级应用有助于图像采集和质量过滤可以合理地用于初级保健或急性护理环境中，以帮助 AOM 的自动诊断和治疗决策。