ArticleDiagnostics (Basel, Switzerland)2022
A Machine Learning Approach to Screen for Otitis Media Using Digital Otoscope Images Labelled by an Expert Panel.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.Scientific reports · 2026Article
- Benchmark-Driven Clinical Decision Framework for Multi-Class Middle Ear Disease Diagnosis: Superiority of Swin Transformer in Accuracy and Stability.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning-Based Semantic Segmentation and Classification of Otoscopic Images for Otitis Media Diagnosis and Health Promotion.Diagnostics (Basel, Switzerland) · 2026Article
- Use of artificial intelligence in healthcare in South Africa: A scoping review.Health SA = SA Gesondheid · 2025Review
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.Annals of biomedical engineering · 2024Article
- Remote Technologies to Enhance Service Delivery for Adults: Clinical Research Perspectives.Seminars in hearing · 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
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Authors and funding
5 authors.
Funding
Abstract
backgroundOtitis media includes several common inflammatory conditions of the middle ear that can have severe complications if left untreated. Correctly identifying otitis media can be difficult and a screening system supported by machine learning would be valuable for this prevalent disease. This study investigated the performance of a convolutional neural network in screening for otitis media using digital otoscopic images labelled by an expert panel.
methodsFive experienced otologists diagnosed 347 tympanic membrane images captured with a digital otoscope. Images with a majority expert diagnosis (
resultsOverall accuracy of the convolutional neural network was above 0.9 in all except one approach. Sensitivity to finding ears with wax or pathology was above 93% in all cases and specificity was 100%. Adding more images to train the convolutional neural network had no positive impact on the results. Modifications such as normalization of datasets and image augmentation enhanced the performance in some instances.
conclusionsA machine learning approach could be used on digital otoscopic images to accurately screen for otitis media.
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