Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos
- Resource Type
- article
- Authors
- Luoying Hao; Yan Hu; Yanwu Xu; Huazhu Fu; Hanpei Miao; Ce Zheng; Jiang Liu
- Source
- Eye and Vision, Vol 9, Iss 1, Pp 1-10 (2022)
- Subject
- AS-OCT videos
Angle-closure
Iris change
Glaucoma
Deep learning
Ophthalmology
RE1-994
- Language
- English
- ISSN
- 2326-0254
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