# Recovery of the Pupillary Response After Light Adaptation Is Slowed in Patients with Age-Related Macular Degeneration

## TL;DR

A new VR headset method shows promise for detecting age-related macular degeneration by measuring how the pupil responds to changes in light.

## Contribution

A novel, non-invasive VR-based method for assessing retinal function in AMD patients using eye-tracking technology.

## Key findings

- Pupillary response amplitude increased with longer dark stimulus exposure.
- AMD patients showed significantly slower pupillary recovery compared to healthy controls.
- The diagnostic accuracy for AMD detection was approximately 92%.

## Abstract

Purpose: This study evaluates a novel, non-invasive method using a virtual reality (VR) headset with integrated eye trackers to assess retinal function by measuring the recovery of the pupillary response after light adaptation in patients with age-related macular degeneration (AMD). Methods: In this pilot study, fourteen patients with clinically confirmed AMD and 14 age-matched healthy controls were exposed to alternating bright and dark stimuli using a VR headset. The dark stimulus duration increased incrementally by 100 milliseconds per trial, repeated over 50 cycles. The pupillary response to the re-onset of brightness was recorded. Data were analyzed using a linear mixed-effects model to compare recovery patterns between groups and a convolutional neural network to evaluate diagnostic accuracy. Results: The pupillary response amplitude increased with longer dark stimuli, i.e., the longer the eye was exposed to darkness the bigger was the subsequent pupillary amplitude. This pupillary recovery was significantly slowed by age and by the presence of macular degeneration. Test diagnostic accuracy for AMD was approximately 92%, with a sensitivity of 90% and a specificity of 70%. Conclusions: This proof-of-concept study demonstrates that consumer-grade VR headsets with integrated eye tracking can detect retinal dysfunction associated with AMD. The method offers a fast, accessible, and potentially scalable approach for retinal disease screening and monitoring. Further optimization and validation in larger cohorts are needed to confirm its clinical utility.