Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (11): 24-32, 38.doi: 10.6040/j.issn.1671-7554.0.2020.1249

• Special topic on new progress in ophthalmic artificial intelligence • Previous Articles     Next Articles

Artificial intelligence deep learning in glaucoma imaging: current progress and future prospect

Carol Y. Cheung*(),Anran RAN   

  1. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
  • Received:2020-09-04 Online:2020-11-10 Published:2020-11-04
  • Contact: Carol Y. Cheung E-mail:carolcheung@cuhk.edu.hk

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Abstract:

Glaucoma is a group of heterogeneous neurodegenerative diseases, characterized by the gradual loss of retinal ganglion cells and their axons, and has now become the major reason of irreversible blindness worldwide. Artificial intelligence (AI) is intelligence demonstrated by machines. Deep learning (DL) is a subset of AI based on deep neural networks, and it has made great breakthroughs in medical imaging. In glaucoma imaging, research interests have been increasing on applying DL in fundus photographs and optical coherence tomography (OCT) for glaucomatous optic neuropathy (GON) detection. Promising results show that the incorporation of DL technology in imaging for glaucoma assessment is efficient and accurate, which could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as setting a standard for ground truth labelling among different studies, visualizing the learning process in the "black box", improving the model generalizability on unseen datasets, developing the DL-powered infrastructure for real-world implementation, establishing a practical clinical workflow, conducting prospective validation and cost-effectiveness analysis. This review summarizes recent studies on the application of AI on glaucoma imaging, discusses the potential clinical impact and future research directions.

Key words: Glaucoma imaging, Artificial intelligence, Optical coherence tomography, Fundus photography, Deep learning

CLC Number: 

  • R574

Fig.1

Artificial intelligence, machine learning and deep learning"

Table 1

The basic data sets for the development and performance evaluation of deep learning models"

术语 说明
训练集 与调试集和内部验证集来自同一个大的数据集，是基于一定比例划分且互不重合，用于建立深度学习模型。研究者可构建具有不同参数甚至不同网络架构的DL模型来学习训练集中青光眼性视神经改变的相关特征。
调试集 与训练集和内部验证集来自同一个大的数据集，是基于一定比例划分且互不重合，用于实时评估不同模型在训练期间的性能。研究者可根据实时观察的学习曲线进行参数调节，确定训练停止节点，并最终选择最佳DL模型。
内部测试集 与训练集和调试集来自同一个大的数据集，是基于一定比例划分且互不重合，用于评估所选的最佳DL模型在该未知数据集上的性能。临床研究中，研究者需汇报模型在内部测试集上的性能，通常包括受试者工作特征曲线下面积，灵敏度，特异性以及准确性等。
外部测试集 一个或多个独立的数据集，与训练集、测试集和内部验证集来自不同数据集，用于评估上述DL模型在其他未知数据集上的性能，验证其临床通用性。研究者同样需汇报模受试者工作特征曲线下面积、灵敏度、特异性以及准确性等。当DL模型在内部以及外部测试集都具有较好性能时，即证明该模型有良好的通用性。
K-倍交叉验证 主要用于数据量有限时。将整个大的数据集分为k个相等的小数据集，其中(k-1)个合并为训练集用于模型开发，剩下的一个作为测试集评估模型性能。该训练过程重复k次，每次选择其中一个小数据集用作测试集。研究者应汇报所有测试集的平均性能，通常为${\bar x}$±s。
留一法交叉验证 主要用于数据量非常有限时，是一种特殊类型的K-倍交叉验证，此时K等于数据总数(即标注图像的总数)。仅留下一张图像来测试模型性能，其他图像全部用于训练。该训练过程同样重复k次，研究者应汇报所有测试集的平均性能，通常为${\bar x}$±s。

Fig.2

The basic steps for the development and performance evaluation of deep learning models"

Fig.3

The deep learning models based on different input of OCT A: Traditional OCT reports; B: Optic disc segmentation-free 2D scans; C: Optic disc volumetric scans."

Fig.4

The workflow of artificial intelligence-based glaucoma imaging analysis system for glaucoma detection or screening in clinics"

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