An artificial intelligence model for the diagnosis of otitis media with effusion in children
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By
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Kitirat Ungkanont
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Akadej Udomchaiporn
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Nopavit Sriphoonga
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Thanakrit Wannarong
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Thaweewat Rugsujrit
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Tachasit Chueprasert
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Archwin Tanphaichitr
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Vannipa Vathanophas
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May 8, 2026
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Objective:
To develop an AI model for predicting the diagnosis of otitis media with effusion (OME) in children.
Key Findings:
- AI model developed using CNN achieved high accuracy in diagnosing OME.
- Characteristic features of the TM, such as color and transparency, were critical for diagnosis.
- Expert otolaryngologists had varying accuracy in diagnosing OME, which improved with training.
Interpretation:
The AI model serves as a preliminary diagnostic tool, potentially aiding less experienced clinicians in diagnosing OME in pediatric patients.
Limitations:
- The study was conducted at a single tertiary care center, which may limit generalizability.
- The model's performance needs validation in diverse clinical settings.
Conclusion:
The developed AI model shows promise in improving the diagnosis of OME in children, addressing challenges faced by clinicians.
