IEEE J Biomed Health Inform. 2022 Feb;26(2):888-897. doi: 10.1109/JBHI.2021.3093007. Epub 2022 Feb 4.
Otosclerosis is a common disease of the middle ear leading to stapedial fixation. Its rapid and non-invasive diagnosis could be achieved through wideband tympanometry (WBT), but the interpretation of the raw data provided by this tool is complex and time-consuming. Convolutional neural networks (CNN) could potentially be applied to this situation to help the clinicians categorize WBT data. A dataset containing 135 samples from 80 patients with otosclerosis and 55 controls was obtained. We designed a lightweight CNN to categorize samples into the otosclerosis and control. Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.95 ±0.011, and the F1-score was 0.89 ±0.031 ( r=10). The performance was further improved by data augmentation schemes and transfer learning strategies (AUC: 0.97 ±0.010, F1-score: 0.94 ±0.016, , ANOVA). Finally, the most relevant diagnostic features employed by the CNN were assessed via the activation pattern heatmaps. These results are crucial for the visual interpretation of WBT graphic outputs which clinicians use in routine, and for a better understanding of the WBT signal in relation to the ossicular mechanics.
耳硬化症是一种常见的中耳疾病，可导致镫骨固定。通过宽带鼓室图（WBT）可以快速且无创地进行诊断，但该工具提供的原始数据的解释既复杂又耗时。卷积神经网络（CNN）可应用于这种情况，帮助临床医生对 WBT 数据进行分类。我们获得了一个包含 80 名耳硬化症患者和 55 名对照者的 135 个样本的数据集。我们设计了一个轻量级的 CNN 来对样本进行分类，分为耳硬化症和对照组。接收者操作特征（ROC）分析显示曲线下面积（AUC）为 0.95±0.011，F1 得分为 0.89±0.031（r=10）。通过数据增强方案和迁移学习策略（AUC：0.97±0.010，F1 得分：0.94±0.016，ANOVA）进一步提高了性能。最后，通过激活模式热图评估了 CNN 所采用的最相关的诊断特征。这些结果对于临床医生在常规中使用的 WBT 图形输出的直观解释非常重要，并且有助于更好地理解 WBT 信号与听小骨力学之间的关系。
