Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pennsylvania.
Tandon School of Engineering, New York University, New York, New York.
JAMA Pediatr. 2024 Apr 1;178(4):401-407. doi: 10.1001/jamapediatrics.2024.0011.
Acute 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.
To 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 PARTICIPANTS: This 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.
Otoscopic examination.
Using 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.
Using 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.
These 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.
急性中耳炎（AOM）是儿童中经常诊断出的疾病，但诊断的准确性一直很低。已经开发出多种神经网络来识别 AOM 的存在，但临床应用有限。
开发和内部验证一种人工智能决策支持工具，以解释鼓膜的视频并提高 AOM 诊断的准确性。
设计、地点和参与者：这项诊断研究分析了 2018 年至 2023 年间宾夕法尼亚州的两个地点的门诊就诊期间使用智能手机捕获的鼓膜耳镜视频。合格的参与者包括因疾病就诊或健康检查就诊的儿童。
耳镜检查。
使用由经过验证的耳科医生注释的耳镜视频，训练深度残差循环神经网络以预测鼓膜的特征和 AOM 与无 AOM 的诊断。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。还训练了一个噪声质量过滤器，以提示用户获取的视频片段可能不适合诊断目的。
使用来自 635 名儿童（多数年龄小于 3 岁）的 1151 个视频，深度残差循环神经网络的诊断准确性与决策树网络几乎相同。最终的深度残差循环神经网络算法将鼓膜视频分类为 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%），决策树模型的灵敏度为 93.7%（95%CI，92.4%-94.9%）和特异性为 93.3%（92.5%-94.1%）。输出的鼓膜特征中，鼓膜凸起与预测诊断最为吻合；在测试集中，预测诊断为 AOM 的 230 例病例中，230 例（100%）均存在鼓膜凸起。
这些发现表明，鉴于其准确性高，该算法和医疗级应用程序可以方便地用于图像采集和质量过滤，可合理地用于初级保健或急性护理环境，以帮助自动诊断 AOM 并做出治疗决策。
