ArticleBiosensors2021
Handheld Briefcase Optical Coherence Tomography with Real-Time Machine Learning Classifier for Middle Ear Infections.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 24 citations in OpenAlex.
- OCT in Oncology and Precision Medicine: From Nanoparticles to Advanced Technologies and AI.Bioengineering (Basel, Switzerland) · 2025Review
- Optical Coherence Tomography Imaging and Angiography of Skull Base Tumors Presenting as a Middle Ear Mass in Clinic.Diagnostics (Basel, Switzerland) · 2025Article
- Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.Journal of biophotonics · 2024Article
- Non-rigid point cloud registration for middle ear diagnostics with endoscopic optical coherence tomography.International journal of computer assisted radiology and surgery · 2024Article
- Image-Based Artificial Intelligence Technology for Diagnosing Middle Ear Diseases: A Systematic Review.Journal of clinical medicine · 2023Review
- In Vivo Optical Characterization of Middle Ear Effusions and Biofilms During Otitis Media.Journal of the Association for Research in Otolaryngology : JARO · 2023Observational
- Automated classification of otitis media with OCT: augmenting pediatric image datasets with gold-standard animal model data.Biomedical optics express · 2022Article
- Multimodal Handheld Probe for Characterizing Otitis Media - Integrating Raman Spectroscopy and Optical Coherence Tomography.Frontiers in photonics · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
A middle ear infection is a prevalent inflammatory disease most common in the pediatric population, and its financial burden remains substantial. Current diagnostic methods are highly subjective, relying on visual cues gathered by an otoscope. To address this shortcoming, optical coherence tomography (OCT) has been integrated into a handheld imaging probe. This system can non-invasively and quantitatively assess middle ear effusions and identify the presence of bacterial biofilms in the middle ear cavity during ear infections. Furthermore, the complete OCT system is housed in a standard briefcase to maximize its portability as a diagnostic device. Nonetheless, interpreting OCT images of the middle ear more often requires expertise in OCT as well as middle ear infections, making it difficult for an untrained user to operate the system as an accurate stand-alone diagnostic tool in clinical settings. Here, we present a briefcase OCT system implemented with a real-time machine learning platform for middle ear infections. A random forest-based classifier can categorize images based on the presence of middle ear effusions and biofilms. This study demonstrates that our briefcase OCT system coupled with machine learning can provide user-invariant classification results of middle ear conditions, which may greatly improve the utility of this technology for the diagnosis and management of middle 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 OpenQuestion graph.