Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.

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 mor

SUBMITTER: Zaki FR 

PROVIDER: S-EPMC11464188 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature