An artificial intelligence model for the diagnosis of otitis media with effusion in children
1
Issued Date
2026-01-01
Resource Type
eISSN
20552076
Scopus ID
2-s2.0-105038223080
Journal Title
Digital Health
Volume
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
Digital Health Vol.12 (2026)
Suggested Citation
Ungkanont K., Udomchaiporn A., Sriphoonga N., Wannarong T., Rugsujrit T., Chueprasert T., Tanphaichitr A., Vathanophas V. An artificial intelligence model for the diagnosis of otitis media with effusion in children. Digital Health Vol.12 (2026). doi:10.1177/20552076261450814 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116756
Title
An artificial intelligence model for the diagnosis of otitis media with effusion in children
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Abstract
Background: The diagnosis of otitis media with effusion (OME) requires substantial training and experience in otoscopic examination of children. Objective: This study developed an artificial intelligence (AI) model to predict OME diagnosis in children. Methods: The source data were images of pediatric patients’ tympanic membranes obtained by otoendoscopy. A convolutional neural network was used in machine learning. The diagnostic features of the tympanic membrane, as labelled by the experts, and the surgical findings served as the ground truth. InceptionV4 built the final model. The model was trained using the Adaptive Moment Estimation optimizer with an initial learning rate of 0.0001 and a total duration of 100 epochs. The batch size was 32. The Categorical Cross-Entropy loss function was employed for the internal validation. The outcome was to distinguish between OME and normal tympanic membrane. A confusion matrix was used to assess the model’s performance. The model was tested for agreement with otolaryngologists and implemented as a web application. Results: The initial sample size was 320 pictures. For OME, the model achieved an accuracy of 94.7% (95% CI 0.88, 1). The F1 score was 96% (95% CI 0.89, 1), and the area under the receiver operating characteristic curve was 0.98 (95% CI 0.93, 1). The kappa agreement between AI and experienced otolaryngologists was 0.627 (p < 0.001). Conclusion: An AI diagnostic model for otitis media with effusion had good accuracy and moderate agreement with otolaryngologists. The model should be helpful for preliminary diagnosis, telemedicine, or educational purposes.