ArticleNPJ digital medicine2019
Automated classification platform for the identification of otitis media using optical coherence tomography.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Diagnosis of tympanic effusions using hyperspectral imaging to prevent unnecessary paracentesis procedures.BMC medical imaging · 2026Article
- Optical Coherence Tomography as an Emerging Technique for Identifying Pediatric Middle Ear Effusion.The Laryngoscope · 2026Article
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.Journal of biophotonics · 2024Article
- Multi-modal deep learning for joint prediction of otitis media and diagnostic difficulty.Laryngoscope investigative otolaryngology · 2024Article
- Label-Free Optical Technologies for Middle-Ear Diseases.Bioengineering (Basel, Switzerland) · 2024Review
- 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
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 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
- A Machine Learning Approach to Screen for Otitis Media Using Digital Otoscope Images Labelled by an Expert Panel.Diagnostics (Basel, Switzerland) · 2022Article
- Performing tympanometry using smartphones.Communications medicine · 2022Article
- Multimodal Handheld Probe for Characterizing Otitis Media - Integrating Raman Spectroscopy and Optical Coherence Tomography.Frontiers in photonics · 2022Article
- Handheld Briefcase Optical Coherence Tomography with Real-Time Machine Learning Classifier for Middle Ear Infections.Biosensors · 2021Article
- In vivo dynamic characterization of the human tympanic membrane using pneumatic optical coherence tomography.Journal of biophotonics · 2021Article
- Advances in Optical Detection of Human-Associated Pathogenic Bacteria.Molecules (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The diagnosis and treatment of otitis media (OM), a common childhood infection, is a significant burden on the healthcare system. Diagnosis relies on observer experience via otoscopy, although for non-specialists or inexperienced users, accurate diagnosis can be difficult. In past studies, optical coherence tomography (OCT) has been used to quantitatively characterize disease states of OM, although with the involvement of experts to interpret and correlate image-based indicators of infection with clinical information. In this paper, a flexible and comprehensive framework is presented that automatically extracts features from OCT images, classifies data, and presents clinically relevant results in a user-friendly platform suitable for point-of-care and primary care settings. This framework was used to test the discrimination between OCT images of normal controls, ears with biofilms, and ears with biofilms and middle ear fluid (effusion). Predicted future performance of this classification platform returned promising results (90%+ accuracy) in various initial tests. With integration into patient healthcare workflow, users of all levels of medical experience may be able to collect OCT data and accurately identify the presence of middle ear fluid and/or biofilms.
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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.