Feasibility of Automated Gonioscopy Imaging in Clinical Practice.

Prcis: Automated gonioscopy provided good-quality images of the anterior chamber angle. There was a short learning curve for operators, and the examination was well tolerated by patients. Patients expressed a preference for automated gonioscopy compared with traditional gonioscopy.

Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation.

Objective: To develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.

Methods And Analysis: We used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth.

On Clinical Agreement on the Visibility and Extent of Anatomical Layers in Digital Gonio Photographs.

Purpose: To quantitatively evaluate the inter-annotator variability of clinicians tracing the contours of anatomical layers of the iridocorneal angle on digital gonio photographs, thus providing a baseline for the validation of automated analysis algorithms.

Methods: Using a software annotation tool on a common set of 20 images, five experienced ophthalmologists highlighted the contours of five anatomical layers of interest: iris root (IR), ciliary body band (CBB), scleral spur (SS), trabecular meshwork (TM), and cornea (C). Inter-annotator variability was assessed by (1) comparing the number of times ophthalmologists delineated each layer in the dataset; (2) quantifying how the consensus area for each layer (i.