Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong.
Hong Kong Eye Hospital, Hong Kong.
Transl Vis Sci Technol. 2021 Sep 1;10(11):16. doi: 10.1167/tvst.10.11.16.
Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left).
A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2).
For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively.
Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities.
The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.
人工智能（AI）深度学习（DL）已被证明在不同临床环境下对视网膜照片中的眼病检测和筛查具有很大的潜力，特别是在初级保健中。然而，为了简化开发的 AI-DL 算法的应用，自动进行预诊断图像评估是必不可少的。在这项研究中，我们开发并验证了一种基于 DL 的视网膜照片预诊断评估模块，该模块针对图像质量（可分级与不可分级）、视野（黄斑中心与视盘中心）和眼睛的侧别（右眼与左眼）。
该 DL 模块使用了来自香港、新加坡和英国的各种临床环境的 1914 名受试者的 21348 张视网膜照片，由两种基于 DL 的算法（EfficientNet-B0 和 MobileNet-V2）进行训练、内部验证和外部测试。
对于图像质量评估，该预诊断模块在内部验证数据集和两个外部测试数据集中的受试者工作特征曲线（AUROC）值分别为 0.975、0.999 和 0.987。对于视野评估，该模块在所有数据集的 AUROC 值均为 1.000。对于眼睛侧别的评估，该模块在内部验证数据集和两个外部测试数据集中的 AUROC 值分别为 1.000、0.999 和 0.985。
我们的研究表明，这种用于评估视网膜照片的图像质量、视野和眼睛侧别的三合一 DL 模块在不同中心和种族之间具有出色的性能和泛化能力。
杨洁
