Which glaucoma patients should be monitored at home and exploration of clinician perceptions on home monitoring? a survey of glaucoma specialists in the UK.

Objectives: To identify suitable patients for glaucoma home monitoring and explore clinicians' perceptions of the possible benefits and risks of home monitoring within the National Health Service.

Design: An online survey composed of open-ended and closed-ended questions.

Setting: Secondary care.

Feasibility of in-home monitoring for people with glaucoma: the I-TRAC mixed-methods study.

Background: Glaucoma is a chronic disease of the optic nerve and a leading cause of severe visual loss in the UK. Once patients have been diagnosed, they need regular monitoring at hospital eye services. Recent advances in technology mean patients with glaucoma can now monitor their disease at home.

Central corneal thickness and the risk of primary open-angle glaucoma: a Mendelian randomisation mediation analysis.

Background: The association of central corneal thickness (CCT) with primary open-angle glaucoma (POAG) remains uncertain. Although several observational studies assessing this relationship have reported an inverse association between CCT and POAG, this could be the result of collider bias. In this study, we leveraged human genetic data to assess through Mendelian randomisation (MR) the effect of CCT on POAG risk and whether this effect is mediated by intraocular pressure (IOP) changes.

PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

Purpose: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness.

Methods: We used deep learning to segment the optic disc, fovea, and vessels in fundus photographs, and measured pallor. We assessed the relationship between pallor and pRNFL thickness derived from optical coherence tomography scans in 118 participants.

Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis.

Prcis: In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively.

Purpose: This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle closure in patients with glaucoma.