A Practical and Simplified Method for Structure-Function Analysis in Patients with Glaucoma and Retinal Disease.

Purpose: To describe an accessible method of structure-function correlation using optical coherence tomography (OCT) and virtual reality perimetry (VRP) for patients with retinal disease and glaucoma and to compare results with those of conventional Humphrey visual fields (HVF).

Methods: Patients with a diagnosis of glaucoma involving the central visual field or macula-involving retinal disease were recruited. Patients underwent ophthalmic examination followed by OCT imaging, HVF, and VRP testing.

Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.

Purpose: To develop and test machine learning classifiers (MLCs) for determining visual field progression.

Methods: In total, 90,713 visual fields from 13,156 eyes were included. Six different progression algorithms (linear regression of mean deviation, linear regression of the visual field index, Advanced Glaucoma Intervention Study algorithm, Collaborative Initial Glaucoma Treatment Study algorithm, pointwise linear regression [PLR], and permutation of PLR) were applied to classify each eye as progressing or stable.