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Deep learning multi-classification of middle ear diseases using synthetic tympanic images
Yoshimaru Mizoguchi1,2, Taku Ito1, Masato Yamada2
1Department of Otorhinolaryngology, Institute of Science Tokyo, Tokyo, Japan.
Acta Oto-Laryngologica
|January 11, 2025
Summary
Generative adversarial networks (GANs) did not improve AI diagnostic accuracy for middle ear diseases when combined with real images. However, AI models trained solely on synthetic tympanic membrane images achieved 70% accuracy.
Area of Science:
- Otolaryngology
- Artificial Intelligence
- Medical Imaging
Background:
- Automatic diagnosis of middle ear diseases is advancing with AI.
- Endoscopic tympanic membrane imaging is key for diagnosis.
Purpose of the Study:
- Develop an automated diagnostic system for middle ear diseases.
- Utilize deep learning on clinical tympanic membrane images.
Main Methods:
- Explored generative adversarial networks (GANs) to create synthetic tympanic images.
- Collected 472 real endoscopic images (normal, AOM, OME, CSOM) from 2016-2021.
- Trained InceptionV3 model on real images, then retrained with 200 StyleGAN3 synthetic images.
Main Results:
- Including synthetic images did not significantly improve diagnostic accuracy over real images alone.
- A model trained exclusively on synthetic images achieved ~70% diagnostic accuracy.
Conclusions:
- GAN-generated synthetic images show potential for developing medical diagnostic AI models.
- Further research is needed to optimize synthetic data integration for improved accuracy.
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