ArticleJournal of clinical medicine2021
An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2022Pooled it
- Deep learning for automated diagnosis and differentiation of otitis media on temporal bone CT.BMC medical imaging · 2026Article
- Development and Validation of a CNN-Based Diagnostic Pipeline for the Diagnosis of Otitis Media.Journal of clinical medicine · 2025Article
- D-S-Net: an efficient dual-stage strategy for high-precision segmentation of gross tumor volumes in lung cancer CT images.BMC cancer · 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
- Development and validation of a smartphone-based deep-learning-enabled system to detect middle-ear conditions in otoscopic images.NPJ digital medicine · 2024Article
- Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.JAMA pediatrics · 2024Article
- Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- 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
- 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
- Use of artificial intelligence for the diagnosis of cholesteatoma.Laryngoscope investigative otolaryngology · 2023Article
- Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data.PLOS digital health · 2023Article
- Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet).Scientific reports · 2022Article
- Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network.Journal of translational medicine · 2022Article
- 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
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the "ResNet18 + Shuffle" network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.
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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.