RESUMEN
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.
RESUMEN
Otitis media (OM) is primarily a bacterial middle-ear infection prevalent among children worldwide. In recurrent and/or chronic OM cases, antibiotic-resistant bacterial biofilms can develop in the middle ear. A biofilm related to OM typically contains one or multiple bacterial strains, the most common include Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis, Pseudomonas aeruginosa, and Staphylococcus aureus. Optical coherence tomography (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 primary bacterial biofilms in vitro and in vivo. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest (RF), and XGBoost) to classify and speciate multiclass bacterial biofilms from the texture features extracted from OCT B-Scan images obtained from in vitro cultures and from clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers can help distinguish bacterial biofilms by incorporating clinical knowledge into classification decisions. Furthermore, both classifiers achieved more than 95% of AUC (area under receiver operating curve), 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, which could provide additional clinically relevant data during real-time in vivo characterization of ear infections.
RESUMEN
In the management of otitis media (OM), identification of causative bacterial pathogens and knowledge of their biofilm formation can provide more targeted treatment approaches. Current clinical diagnostic methods rely on the visualization of the tympanic membrane and lack real-time assessment of the causative pathogen(s) and the nature of any biofilm that may reside behind the membrane and within the middle ear cavity. In recent years, optical coherence tomography (OCT) has been demonstrated as an improved in vivo diagnostic tool for visualization and morphological characterization of OM biofilms and middle ear effusions; but lacks specificity about the causative bacterial species. This study proposes the combination of OCT and Raman spectroscopy (RS) to examine differences in the refractive index, optical attenuation, and biochemical composition of Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis, and Pseudomonas aeruginosa; four of the leading otopathogens in OM. This combination provides a dual optical approach for identifying and differentiating OM-causing bacterial species under three different in vitro growth environments (i.e., agar-grown colonies, planktonic cells from liquid cultures, and biofilms). This study showed that RS was able to identify key biochemical variations to differentiate all four OM-causing bacteria. Additionally, biochemical spectral changes (RS) and differences in the mean attenuation coefficient (OCT) were able to distinguish the growth environment for each bacterial species.