ArticleAmerican journal of ophthalmology2026
RNFL Thickness in a Population-Based Cohort: The Canadian Longitudinal Study on Aging M2M (Machine-to-Machine) Study.
Article in American journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Diagnostic Likelihood Ratios for Glaucoma Using Deep Learning-Predicted Retinal Nerve Fiber Layer Thickness from Fundus Photographs.Ophthalmology science · 2026Article
- Artificial Intelligence in Glaucoma Detection and Progression.Advances in ophthalmology and optometry · 2026Article
- Optic Disc Fundus Images Retain Biometric Identity Signals Under Deep Learning.Research square · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo evaluate factors associated with retinal nerve fiber layer (RNFL) thickness in the Canadian Longitudinal Study on Aging (CLSA) using the Machine-to-Machine (M2M) deep learning model applied to fundus photographs.
designCross-sectional study. SUBJECTS: Participants from the baseline Comprehensive Cohort of the CLSA.
methodsThis study included 28,114 CLSA participants aged 45 to 85 years with gradable baseline fundus photographs. The M2M model, trained on optical coherence tomography (OCT) data, was applied to estimate RNFL thickness from disc-centered fundus images. For participants with images from both eyes, the mean RNFL thickness of the 2 eyes was used. Associations between M2M-predicted RNFL thickness and age, sex, ethnicity/race, and self-reported glaucoma were analyzed using linear regression models adjusted for covariates.
main outcome measuresM2M-predicted RNFL thickness, age, age groups, sex, ethnicity/race, and self-reported glaucoma.
resultsThe mean age of participants was 62.6 ± 10.1 years, and 51% were women. Self-reported glaucoma was present in 4.8% of the participants. The mean M2M-predicted RNFL thickness was 90.9 ± 9.2 µm. Age was inversely associated with RNFL thickness (Pearson's r = -0.16; p < .001), with each additional year associated with a 0.15 µm decrease (p < .001); after adjustment for covariates, the association remained significant (β = -0.11; p < .001). Participants with self-reported glaucoma exhibited significantly thinner RNFL (82.6 ± 12.9 µm) compared to those without (91.4 ± 8.7 µm; p < .001). RNFL thickness was slightly greater in women than in men (p < .001), and differences were observed across ethnicity/race groups (p < .001).
conclusionsThe M2M model provided robust estimates of RNFL thickness from fundus photographs in a large population-based cohort. The observed associations between RNFL thickness, age, and glaucoma status were consistent with previous OCT-based findings, supporting the utility of the model for scalable structural assessments in epidemiological studies.
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