Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts;
Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts.
Pediatrics. 2021 Apr;147(4). doi: 10.1542/peds.2020-034546. Epub 2021 Mar 17.
Misdiagnosis of acute and chronic otitis media in children can result in significant consequences from either undertreatment or overtreatment. Our objective was to develop and train an artificial intelligence algorithm to accurately predict the presence of middle ear effusion in pediatric patients presenting to the operating room for myringotomy and tube placement.
We trained a neural network to classify images as " normal" (no effusion) or "abnormal" (effusion present) using tympanic membrane images from children taken to the operating room with the intent of performing myringotomy and possible tube placement for recurrent acute otitis media or otitis media with effusion. Model performance was tested on held-out cases and fivefold cross-validation.
The mean training time for the neural network model was 76.0 (SD ± 0.01) seconds. Our model approach achieved a mean image classification accuracy of 83.8% (95% confidence interval [CI]: 82.7-84.8). In support of this classification accuracy, the model produced an area under the receiver operating characteristic curve performance of 0.93 (95% CI: 0.91-0.94) and F1-score of 0.80 (95% CI: 0.77-0.82).
Artificial intelligence-assisted diagnosis of acute or chronic otitis media in children may generate value for patients, families, and the health care system by improving point-of-care diagnostic accuracy. With a small training data set composed of intraoperative images obtained at time of tympanostomy tube insertion, our neural network was accurate in predicting the presence of a middle ear effusion in pediatric ear cases. This diagnostic accuracy performance is considerably higher than human-expert otoscopy-based diagnostic performance reported in previous studies.
儿童急性和慢性中耳炎的误诊可能导致治疗不足或过度治疗的严重后果。我们的目的是开发和训练人工智能算法，以准确预测因反复发作急性中耳炎或分泌性中耳炎而需行鼓膜切开和置管术的儿科患者中耳积液的存在。
我们使用在手术室接受鼓膜切开和可能置管的儿童的鼓膜图像，训练神经网络将图像分类为“正常”（无积液）或“异常”（存在积液）。模型性能在保留病例和五重交叉验证上进行测试。
神经网络模型的平均训练时间为 76.0（SD ± 0.01）秒。我们的模型方法平均图像分类准确率为 83.8%（95%置信区间：82.7-84.8）。为支持这种分类准确性，该模型产生了 0.93（95%置信区间：0.91-0.94）的接收器工作特征曲线下面积和 0.80（95%置信区间：0.77-0.82）的 F1 分数。
人工智能辅助诊断儿童急性或慢性中耳炎可能通过提高即时诊断准确性为患者、家庭和医疗保健系统带来价值。我们的神经网络使用由鼓膜切开术时获得的术中图像组成的小训练数据集，准确预测了儿科耳部病例中耳积液的存在。这种诊断准确性表现明显高于之前研究中报道的基于人类专家耳镜检查的诊断性能。
