ArticleBMJ open2021
Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.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 · 2023Trial
- 4DO-DETR for otitis media detection.Scientific reports · 2026Article
- Development and validation of a smartphone-based deep-learning-enabled system to detect middle-ear conditions in otoscopic images.NPJ digital medicine · 2024Article
- Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.BMC medical informatics and decision making · 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
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon-Cosine Wavelet.Entropy (Basel, Switzerland) · 2022Article
- Feasibility of the Machine Learning Network to Diagnose Tympanic Membrane Lesions without Coding Experience.Journal of personalized medicine · 2022Article
- A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.JAMA otolaryngology-- head & neck surgery · 2022Article
- Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model.Frontiers in molecular biosciences · 2022Article
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14 authors.
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Abstract
objectivesThis study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.
designA classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset. On the basis of a Class Activation Map (CAM), a two-stage classification pipeline was developed to improve accuracy and reliability, and simulate an expert reading the TM images. SETTING AND
participantsThis is a retrospective study using otoendoscopic images obtained from the Department of Otorhinolaryngology in China. A dataset was generated with 6066 otoscopic images from 2022 participants comprising four kinds of TM images, that is, normal eardrum, otitis media with effusion (OME) and two stages of chronic suppurative otitis media (CSOM).
resultsThe proposed method achieved an overall accuracy of 93.4% using ResNet50 as the backbone network in a threefold cross-validation. The F1 Score of classification for normal images was 94.3%, and 96.8% for OME. There was a small difference between the active and inactive status of CSOM, achieving 91.7% and 82.4% F1 scores, respectively. The results demonstrate a classification performance equivalent to the diagnosis level of an associate professor in otolaryngology.
conclusionsCNNs provide a useful and effective tool for the automated classification of TM images. In addition, having a weakly supervised method such as CAM can help the network focus on discriminative parts of the image and improve performance with a relatively small database. This two-stage method is beneficial to improve the accuracy of diagnosis of otitis media for junior otolaryngologists and physicians in other disciplines.
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