Deep learning multi-classification of middle ear diseases using synthetic tympanic images.

Abstract

Background: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.

Aim: We aimed to develop an automated diagnostic system for middle ear diseases by applying deep learning techniques to tympanic membrane images obtained during routine clinical practice.

Material and methods: To augment the training dataset, we explored the use of generative adversarial networks (GANs) to produce high-quality synthetic tympanic images that were subsequently added to the training data. Between 2016 and 2021, we collected 472 endoscopic images representing four tympanic membrane conditions: normal, acute otitis media, otitis media with effusion, and chronic suppurative otitis media. These images were utilized for machine learning based on the InceptionV3 model, which was pretrained on ImageNet. Additionally, 200 synthetic images generated using StyleGAN3 and considered appropriate for each disease category were incorporated for retraining.

Results: The inclusion of synthetic images alongside real endoscopic images did not significantly improve the diagnostic accuracy compared to training solely with real images. However, when trained solely on synthetic images, the model achieved a diagnostic accuracy of approximately 70%.

Conclusions and significance: Synthetic images generated by GANs have potential utility in the development of machine-learning models for medical diagnosis.