ArticleJournal of biophotonics2024
Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.
Article in Journal of biophotonics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Article
- Surface versus Nanocatalyst-Induced Matrix Bubbles Govern Temperature-Dependent Biofilm Removal.ACS applied materials & interfaces · 2026Article
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Integrating optical coherence tomography and bioluminescence with predictive modeling for quantitative assessment of methicillin-resistantJournal of biomedical optics · 2025Article
- Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.Antibiotics (Basel, Switzerland) · 2024Review
- Influence of phosphate on bacterial release from activated carbon point-of-use filters and on biofilm characteristics.The Science of the total environment · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.