Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia.
QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Transl Vis Sci Technol. 2024 Jun 3;13(6):1. doi: 10.1167/tvst.13.6.1.
Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). However, developing robust algorithms requires a large number of data sets. We sought to train an adversarial model for generating high-quality optic disc images from a large, diverse data set and then assessed the performance of models on generated synthetic images for detecting GON.
A total of 17,060 (6874 glaucomatous and 10,186 healthy) fundus images were used to train deep convolutional generative adversarial networks (DCGANs) for synthesizing disc images for both classes. We then trained two models to detect GON, one solely on these synthetic images and another on a mixed data set (synthetic and real clinical images). Both the models were externally validated on a data set not used for training. The multiple classification metrics were evaluated with 95% confidence intervals. Models' decision-making processes were assessed using gradient-weighted class activation mapping (Grad-CAM) techniques.
Following receiver operating characteristic curve analysis, an optimal cup-to-disc ratio threshold for detecting GON from the training data was found to be 0.619. DCGANs generated high-quality synthetic disc images for healthy and glaucomatous eyes. When trained on a mixed data set, the model's area under the receiver operating characteristic curve attained 99.85% on internal validation and 86.45% on external validation. Grad-CAM saliency maps were primarily centered on the optic nerve head, indicating a more precise and clinically relevant attention area of the fundus image.
Although our model performed well on synthetic data, training on a mixed data set demonstrated better performance and generalization. Integrating synthetic and real clinical images can optimize the performance of a deep learning model in glaucoma detection.
Optimizing deep learning models for glaucoma detection through integrating DCGAN-generated synthetic and real-world clinical data can be improved and generalized in clinical practice.
深度学习架构可以自动学习与青光眼视神经病变（GON）相关的复杂特征和模式。然而，开发稳健的算法需要大量的数据集。我们试图从一个大型、多样化的数据集训练对抗模型，以生成高质量的视盘图像，然后评估模型在生成的合成图像上检测 GON 的性能。
使用总共 17060 张（6874 张青光眼和 10186 张健康）眼底图像来训练深度卷积生成对抗网络（DCGAN），以合成两类视盘图像。然后，我们训练了两个模型来检测 GON，一个仅在这些合成图像上，另一个在混合数据集（合成和真实临床图像）上。两个模型都在未用于训练的数据集上进行了外部验证。使用 95%置信区间评估了多种分类指标。使用梯度加权类激活映射（Grad-CAM）技术评估模型的决策过程。
在进行受试者工作特征曲线分析后，我们发现用于从训练数据中检测 GON 的最佳杯盘比阈值为 0.619。DCGAN 为健康和青光眼眼睛生成了高质量的合成视盘图像。当在混合数据集上进行训练时，模型的内部验证受试者工作特征曲线下面积达到 99.85%，外部验证达到 86.45%。Grad-CAM 显着图主要集中在视神经头部，表明眼底图像的注意力区域更精确且更具临床相关性。
尽管我们的模型在合成数据上表现良好，但在混合数据集上进行训练可以提高和推广性能。整合合成和真实临床图像可以优化深度学习模型在青光眼检测中的性能。
翻译后内容仅供参考，如有需要请提供更多信息，以便我更好地回答你的问题。
