Deep learning multi-classification of middle ear diseases using synthetic tympanic images.
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...
| Publicado en: | Acta Oto-Laryngologica Vol. 145; no. 2; pp. 134 - 140 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. 人工智能的最新发展使得用内窥镜鼓膜成像来自动诊断中耳疾病成为可能。 通过将深度学习技术应用于常规临床实践中获得的鼓膜图像来开发中耳疾病的自动诊断系统。 为了扩充训练数据集, 我们探索了使用生成对抗网络 (GAN) 来做出高质量的合成鼓膜图像, 随后将其添加到训练数据中。2016 年至 2021 年期间, 我们收集了 472 张内窥镜图像, 代表四种鼓膜状况: 正常、急性中耳炎、渗出性中耳炎和慢性化脓性中耳炎。这些图像用于以 InceptionV3 模型为基础的机器学习, 该模型在 ImageNet 上进行了预训练。此外, 使用 StyleGAN3 生成的并适合每种疾病类别的 200 张合成图像被用于再训练。 与仅使用真实图像进行训练相比, 将合成图像与真实内窥镜图像结合在一起并没有显著提高诊断准确率。然而, 当只使用合成图像进行训练时, 该模型的诊断准确率达到约 70%。 由GAN 生成的合成图像在开发用于医学诊断的机器学习模型方面具有潜在用途。 |
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