Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos.

Luoying Hao, Yan Hu, Yanwu Xu, Huazhu Fu, Hanpei Miao, Ce Zheng, Jiang Liu
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引用次数: 2

Abstract

Background: To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance.

Methods: A total of 369 AS-OCT videos (19,940 frames)-159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)-were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance.

Results: For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s2 vs. 5.256 mm/s2; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610-0.923) vs. 0.820 (95% CI: 0.680-0.961) vs. 0.905 (95% CI: 0.802-1.000) (for Casia dataset) and 0.767 (95% CI: 0.620-0.914) vs. 0.837 (95% CI: 0.713-0.961) vs. 0.919 (95% CI: 0.831-1.000) (for Zeiss dataset).

Conclusions: The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification.

Abstract Image

Abstract Image

Abstract Image

基于AS-OCT视频的虹膜变化动态分析及自动闭角分类深度学习系统。
背景:利用前段光学相干断层扫描(as - oct)视频研究虹膜动态变化与原发性闭角病(PACD)的关系,开发闭角筛查的深度学习自动化系统并验证其性能。方法:共369个AS-OCT视频(19,940帧),其中159个为闭角受试者,210个为正常对照(两个数据集使用不同的AS-OCT捕获设备)。在资深眼科医师的指导下,根据动态临床参数(瞳孔直径)分析虹膜变化(瞳孔收缩)与PACD的相关性。然后开发了一个时间网络来从视频中学习判别时间特征。将数据集随机分成训练集,并使用测试集和五重分层交叉验证来评估性能。结果:在动态临床参数评价中,闭角眼的平均瞳孔收缩速度(VPC) (0.470 mm/s)明显低于正常眼(0.571 mm/s) (P < 2∶5.256 mm/s2;P结论:闭角眼的虹膜在光照下的拉伸程度明显低于正常眼。此外,虹膜运动的动态特征有助于闭角分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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