Deep Learning-Predicted RNFL Loss and Incident Glaucoma in the Canadian Longitudinal Study on Aging

To characterize longitudinal retinal nerve fiber layer (RNFL) thinning predicted from fundus photographs by a machine-to-machine (M2M) model in the Canadian Longitudinal Study on Aging and examine its association with incident glaucoma. Prospective, population-based cohort study of 18,247 participants (30,202 eyes) aged 45–86 years from 11 Canadian sites. Baseline data from 2012-2015, follow-up through 2015-2018. Fundus photographs analyzed with an optical coherence tomography (OCT)-trained M2M algorithm at baseline and after a 3-year follow-up to estimate RNFL thickness change. Demographic factors, intraocular pressure (IOP), and corneal hysteresis (CH) were assessed. Annual rate of predicted RNFL thickness change and risk of incident glaucoma. Linear mixed-effects models identified predictors of RNFL thinning. Incident glaucoma was defined as new self-reported diagnosis at follow-up. Risk factors were evaluated using cox proportional hazards models. Predicted RNFL loss was faster in glaucomatous versus non-glaucomatous eyes (-0.46 ± 2.28 vs -0.18 ± 2.07 μm/year; P < 0.001). In multivariable analysis, faster thinning was associated with older age (β=-0.223 μm/year per decade; P < 0.001), higher baseline IOP (β=-0.020 μm/year per mmHg; P < 0.001), lower CH (β=-0.026 μm/year per mmHg lower; P < 0.001), and thicker baseline predicted-RNFL (β=-0.665 μm/year per 10 μm; P < 0.001). A significant age×IOP interaction (β=-0.007 μm/year per mmHg per decade; P = 0.036) indicated greater IOP-related thinning in older participants. Among 17,552 participants without baseline disease, 344 (1.9%) converted to glaucoma. Faster predicted-RNFL loss was independently associated with incident glaucoma during follow-up (HR = 1.125 per 1 μm/year increase, 95% CI 1.070–1.183, P < 0.001). Deep learning-derived RNFL estimates from fundus photographs were associated with clinically meaningful structural change and predicted incident glaucoma in a population-based cohort. These findings highlight the potential of fundus-based deep learning models to enable glaucoma risk stratification in settings where OCT is impractical or unavailable.