Abstract：
Otitis media (OM), a highly prevalent inflammatory middle-ear disease in children worldwide, is commonly caused by an infection, and can lead to antibiotic-resistant bacterial biofilms in recurrent/chronic OM cases. A biofilm related to OM typically contains one or multiple bacterial species. OCT has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from bacterial biofilms. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest, and XGBoost) to classify multiple species bacterial biofilms from in vitro cultures and clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers achieved more than 95% of AUC, detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, offering valuable insights for real-time in vivo characterization of ear infections.
摘要：
中耳炎(OM),全世界儿童中非常普遍的炎症性中耳疾病，通常是由感染引起的，并可在复发性/慢性OM病例中导致抗生素抗性细菌生物膜。与OM相关的生物膜通常包含一种或多种细菌物种。OCT已在临床上用于可视化中耳中细菌生物膜的存在。本研究使用OCT比较细菌生物膜的微结构图像纹理特征。所提出的方法应用了基于监督机器学习的框架(SVM，随机森林,和XGBoost）对来自体外培养物的多种细菌生物膜和来自人类受试者的临床获得的体内图像进行分类。我们的研究结果表明，优化的SVM-RBF和XGBoost分类器实现了95%以上的AUC，检测每个生物膜类。这些结果表明，通过OCT图像的纹理分析和机器学习框架，可以区分OM引起的细菌生物膜。为耳部感染的实时体内表征提供有价值的见解。
