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Updated: Oct 12, 2025

Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow
Published on: May 26, 2023
Conjunctival ultraviolet autofluorescence area decreases with age and sunglasses use
Gareth Lingham1, Jason Kugelman2, Jason Charng3
1Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Western Australia, Australia garethlingham@lei.org.au.
Background:
Conjunctival ultraviolet autofluorescence (CUVAF) is a method of detecting conjunctival damage related to ultraviolet radiation exposure. In cross-sectional studies, CUVAF area is positively associated with self-reported time spent outdoors and pterygium and negatively associated with myopia; however, longitudinal studies are scarce.
Aims:
To use a novel deep learning-based tool to assess 8-year change in CUVAF area in young adults, investigate factors associated with this change and identify the number of new onset pterygia.
Methods:
A deep learning-based CUVAF tool was developed to measure CUVAF area. CUVAF area and pterygium status were assessed at three study visits: baseline (participants were approximately 20 years old) and at 7-year and 8-year follow-ups. Participants self-reported sun protection behaviours and ocular history.
Results:
CUVAF data were available for 1497 participants from at least one study visit; 633 (43%) participants had complete CUVAF data. Mean CUVAF areas at baseline and the 7-year and 8-year follow-ups were 48.4, 39.3 and 37.7 mm2, respectively. There was a decrease in mean CUVAF area over time (change in total CUVAF area=-0.96 mm2 per year (95% CI: -1.07 to -0.86)). For participants who wore sunglasses ≥1/2 of the time, CUVAF area decreased by an additional -0.42 mm2 per year (95% CI: -0.72 to -0.12) on average. Fourteen (1.5%) participants developed a pterygium.
Conclusions:
In this young adult cohort, CUVAF area declined over an 8-year period. Wearing sunglasses was associated with a faster reduction in CUVAF area. Deep learning-based models can assist in accurate and efficient measurement of CUVAF area.
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