ArticlePloS one2020
OtoMatch: Content-based eardrum image retrieval using deep learning.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 51 citations in OpenAlex.
- StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.Scientific reports · 2026Article
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- Digital Otoscopy With Computer-Aided Composite Image Generation: Impact on the Correct Diagnosis, Confidence, and Time.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Multi-modal deep learning for joint prediction of otitis media and diagnostic difficulty.Laryngoscope investigative otolaryngology · 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
- Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.Scientific reports · 2023Article
- Research and application progress in deep learning in otology.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- Multi-modal medical image classification using deep residual network and genetic algorithm.PloS one · 2023Article
- ExpertosLF: dynamic late fusion of CBIR systems using online learning with relevance feedback.Multimedia tools and applications · 2023Article
- Digital Otoscopy Videos Versus Composite Images: A Reader Study to Compare the Accuracy of ENT Physicians.The Laryngoscope · 2021Article
- Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Acute infections of the middle ear are the most commonly treated childhood diseases. Because complications affect children's language learning and cognitive processes, it is essential to diagnose these diseases in a timely and accurate manner. The prevailing literature suggests that it is difficult to accurately diagnose these infections, even for experienced ear, nose, and throat (ENT) physicians. Advanced care practitioners (e.g., nurse practitioners, physician assistants) serve as first-line providers in many primary care settings and may benefit from additional guidance to appropriately determine the diagnosis and treatment of ear diseases. For this purpose, we designed a content-based image retrieval (CBIR) system (called OtoMatch) for normal, middle ear effusion, and tympanostomy tube conditions, operating on eardrum images captured with a digital otoscope. We present a method that enables the conversion of any convolutional neural network (trained for classification) into an image retrieval model. As a proof of concept, we converted a pre-trained deep learning model into an image retrieval system. We accomplished this by changing the fully connected layers into lookup tables. A database of 454 labeled eardrum images (179 normal, 179 effusion, and 96 tube cases) was used to train and test the system. On a 10-fold cross validation, the proposed method resulted in an average accuracy of 80.58% (SD 5.37%), and maximum F1 score of 0.90 while retrieving the most similar image from the database. These are promising results for the first study to demonstrate the feasibility of developing a CBIR system for eardrum images using the newly proposed methodology.
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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.