Research Group Ophthalmology, KU Leuven, Leuven, Belgium.
VITO NV, Mol, Belgium.
Acta Ophthalmol. 2020 Feb;98(1):e94-e100. doi: 10.1111/aos.14193. Epub 2019 Jul 25.
To assess the use of deep learning (DL) for computer-assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the explainability of the glaucoma classifier.
This original investigation pooled 8433 retrospectively collected and anonymized colour optic disc-centred fundus images, in order to develop a deep learning-based classifier for glaucoma diagnosis. The labels of the various deep learning models were compared with the clinical assessment by glaucoma experts. Data were analysed between March and October 2018. Sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and amount of data used for discriminating between glaucomatous and non-glaucomatous fundus images, on both image and patient level.
Trained using 2072 colour fundus images, representing 42% of the original training data, the trained DL model achieved an AUC of 0.995, sensitivity and specificity of, respectively, 98.0% (CI 95.5%-99.4%) and 91% (CI 84.0%-96.0%), for glaucoma versus non-glaucoma patient referral.
These results demonstrate the benefits of deep learning for automated glaucoma detection based on optic disc-centred fundus images. The combined use of transfer and active learning in the medical community can optimize performance of DL models, while minimizing the labelling cost of domain-specific mavens. Glaucoma experts are able to make use of heat maps generated by the deep learning classifier to assess its decision, which seems to be related to inferior and superior neuroretinal rim (within ONH), and RNFL in superotemporal and inferotemporal zones (outside ONH).
评估深度学习（DL）在计算机辅助青光眼识别中的应用，以及使用主动学习策略选择图像进行训练的效果，该策略可最大限度地降低标记成本。此外，本研究还关注青光眼分类器的可解释性。
本原始研究汇集了 8433 张回顾性收集和匿名的彩色视盘中心眼底图像，用于开发基于深度学习的青光眼诊断分类器。各种深度学习模型的标签与青光眼专家的临床评估进行了比较。数据分析于 2018 年 3 月至 10 月进行。在图像和患者两个层面上，分析了用于区分青光眼和非青光眼眼底图像的敏感性、特异性、接收者操作特征曲线（ROC）下面积（AUC）以及用于区分的数据集量。
使用 2072 张彩色眼底图像（占原始训练数据的 42%）进行训练后，训练好的 DL 模型的 AUC 为 0.995，对青光眼与非青光眼患者转诊的敏感性和特异性分别为 98.0%（95.5%-99.4%）和 91%（84.0%-96.0%）。
这些结果表明，深度学习在基于视盘中心眼底图像的自动青光眼检测方面具有优势。在医学领域中，迁移学习和主动学习的结合可以优化 DL 模型的性能，同时最大限度地降低领域专家的标记成本。青光眼专家可以利用深度学习分类器生成的热图来评估其决策，该决策似乎与视盘内的下和上神经视网膜边缘（ONH 内）以及超颞和下颞区的 RNFL（ONH 外）有关。
