Our results confirm that horizontal eye movements generate significant ONH strains, which is consistent with our previous estimations using finite element analysis. Further studies are needed to explore a possible link between ONH strains induced by eye movements and axonal loss in optic neuropathies.

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. Further, we automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues. We offer here a robust segmentation framework that could also be extended to the 3D segmentation of the ONH tissues.

Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for the presence of angle closure. DESIGN: Development of an artificial intelligence automated detection system for the presence of angle closure. METHODS: A deep learning system for automated angleclosure detection in AS-OCT images was developed, and this was compared with another automated angle-closure detection system based on quantitative features. A total of 4135 Visante AS-OCT images from 2113 subjects (8270 anterior chamber angle images with 7375 openangle and 895 angle-closure) were examined. The deep learning angle-closure detection system for a 2-class classification problem was tested by 5-fold cross-validation. The deep learning system and the automated angle-closure detection system based on quantitative features were evaluated against clinicians' grading of AS-OCT images as the reference standard. RESULTS: The area under the receiver operating characteristic curve of the system using quantitative features was 0.90 (95% confidence interval [CI] 0.891-0.914) with a sensitivity of 0.79 ± 0.037 and a specificity of 0.87 ± 0.009, while the area under the receiver operating characteristic curve of the deep learning system was 0.96 (95% CI 0.953-0.968) with a sensitivity of 0.90 ± 0.02 and a specificity of 0.92 ± 0.008, against clinicians' grading of AS-OCT images as the reference standard. CONCLUSIONS: The results demonstrate the potential of the deep learning system for angle-closure detection in AS-OCT images.

Adaptive compensation is superior to EDI in improving LC visibility. Visibility of the posterior LC remains poor suggesting impracticality in using LC thickness as a glaucoma biomarker.

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch’s membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch’s membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra’s algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice’s Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

Seventy-three (47 females, 26 males) subjects were recruited, the majority of whom were Chinese (89%). The authors excluded 29 images (19.9%) owing to poor image quality, leaving 117 HD-OCT images (65 nasal, 52 temporal) for analysis. SL and SS could be identified in 95% and 85% of quadrants respectively (p = 0.035). SL-AOD and SL-TISA were significantly correlated with SS parameters (all r ≥ 0.85) and gonioscopic grading (all r ≥ 0.69). In eyes with closed angles (n = 36), SL parameters showed strong correlations with gonioscopic grading (r ranged from 0.43 to 0.44). Conclusions Novel angle parameters, based on SL as a landmark, may be useful to quantify ACA width and to assess for risk of angle closure.

In SS-OCT HD images, the mean TM width varied from 710 to 890 μm in the different quadrants of the eye, and the inferior quadrant TM was the widest compared to other quadrants.

It was more difficult to determine angle closure status with iVue compared with Cirrus SD-OCT. There was fair agreement between both devices with gonioscopy for identifying angle closure.