ArticleJournal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale2019
Building an Otoscopic screening prototype tool using deep learning.
Article in Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale, 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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Who cites it
18 citing papers in PubMed.
- A randomised trial to assess the educational benefit of a smartphone otoscope in undergraduate medical training.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 · 2021Trial
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.JAMA pediatrics · 2024Article
- 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
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic Measuring of Finger Joint Space Width on Hand Radiograph using Deep Learning and Conventional Computer Vision Methods.Biomedical signal processing and control · 2023Article
- 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
- 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
- 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
- ENT from afar: Opportunities for remote patient assessment, clinical management, teaching and learning.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2021Article
- Handheld Briefcase Optical Coherence Tomography with Real-Time Machine Learning Classifier for Middle Ear Infections.Biosensors · 2021Article
- Deep learning-enabled medical computer vision.NPJ digital medicine · 2021Review
- Pediatric Otoscopy Video Screening With Shift Contrastive Anomaly Detection.Frontiers in digital health · 2021Article
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOtologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network. MATERIAL AND
methodsA database of 734 unique otoscopic images of various ear pathologies, including 63 cerumen impactions, 120 tympanostomy tubes, and 346 normal tympanic membranes were acquired. 80% of the images were used for the training of a convolutional neural network and the remaining 20% were used for algorithm validation. Image augmentation was employed on the training dataset to increase the number of training images. The general network architecture consisted of three convolutional layers plus batch normalization and dropout layers to avoid over fitting.
resultsThe validation based on 45 datasets not used for model training revealed that the proposed deep convolutional neural network is capable of identifying and differentiating between normal tympanic membranes, tympanostomy tubes, and cerumen impactions with an overall accuracy of 84.4%.
conclusionOur study shows that deep convolutional neural networks hold immense potential as a diagnostic adjunct for otologic disease management.
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