ArticlePloS one2020
Computer-aided diagnosis of external and middle ear conditions: A machine learning approach.
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 31 papers.
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Who cites it
31 citing papers in PubMed.
- StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.Scientific reports · 2026Article
- Artificial Intelligence and Machine Learning in Audiology and Hearing Disorders: A Scoping Review with Bibliometric and Thematic Mapping (1995-2025).Audiology research · 2026Review
- Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- 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
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients.Diagnostics (Basel, Switzerland) · 2024Article
- An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Review
- Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.Annals of biomedical engineering · 2024Article
- Label-Free Optical Technologies for Middle-Ear Diseases.Bioengineering (Basel, Switzerland) · 2024Review
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Image-Based Artificial Intelligence Technology for Diagnosing Middle Ear Diseases: A Systematic Review.Journal of clinical medicine · 2023Review
- A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results.Diagnostics (Basel, Switzerland) · 2023Article
- 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
- Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data.PLOS digital health · 2023Article
- Feasibility of the Machine Learning Network to Diagnose Tympanic Membrane Lesions without Coding Experience.Journal of personalized medicine · 2022Article
- Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: A retrospective deep learning study.EClinicalMedicine · 2022Article
- Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.Journal of digital imaging · 2022Article
- A Machine Learning Approach to Screen for Otitis Media Using Digital Otoscope Images Labelled by an Expert Panel.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage -i.e., diagnosis- using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.
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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.