A Depth-Dependent Integrated VF Simulation for Analysis and Visualization of Glaucomatous VF Defects

Abstract

Citation: Liu P, McKendrick A, Ma-Wyatt A, Turpin A. A depth-dependent integrated VF simulation for analysis and visualization of glaucomatous VF defects. Trans Vis Sci Tech. 2020;9(3):8, https://doi.org/10.1167/tvst.9.3.8Purpose: Visual fields (VF) are measured monocularly at a single depth, yet real-life activities require people to interact with objects binocularly at multiple depths. To better characterize visual functioning in clinical vision conditions such as glaucoma, analyzing visual impairment in a depth-dependent fashion is required. We developed a depthdependent integrated VF (DD-IVF) simulation and demonstrated its usefulness by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 VF. Methods:The 12 archetypes included typical variants of superior and inferior nasal steps, arcuate and altitudinal defects, temporal wedge, biarcuate, and intact VFs. DD-IVF simulation maps the monocular 24-2 VF archetypes to binocular ones as a function of depth by incorporating three parameters of fixation, object, and interpupillary distances. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular VF and returned as the higher value of the two. Results:The simulation produced 144 DD-IVFs for multiple depths from combinations of 12 glaucomatous archetypes. The DD-IVFs are included as a Shiny app in the binovisualfields package. The number of impaired locations in the DD-IVFs varied according to the overlap of VF loss between eyes. Conclusions:Our DD-IVF program revealed binocular functional visual defects associated with glaucomatous archetypes of the 24-2 pattern and is designed to do the same for empirically measured VFs. The comparison of identified visual impairments across depths may be informative for future empirical exploration of functional visual impairments in depth in glaucoma and other conditions leading to bilateral VF loss.Translational Relevance: Our DD-IVF program can reveal depth-dependent functional visual defects for clinical vision conditions where 24-2 test patterns are available.

PyVisualFields: A Python Package for Visual Field Analysis

“…As mentioned before, two available open-source R packages ( vfprogression and visualField ) wrapped in our library. There are also more R packages related to VF analysis and simulation, such as binovisualfields 15 , 16 and spCP 17 , 18 and adding their functionalities to our package is a milestone for future developments.…”