Dynamic Analysis of Iris Changes and a Deep Learning System for Automated Angle-closure Classification Based on AS-OCT Videos

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/s vs. 5.256 mm/s; 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.