Ophthalmology. Glaucoma · 2026
Deep Learning-Predicted RNFL Loss and Incident Glaucoma in the Canadian Longitudinal Study on Aging.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Samico Gustavo A, da Costa Douglas R, Scherer Rafael, Azizi Ali, Rabinowitz Aaron S, Malek Davina A, Gracitelli Carolina P B, Medeiros Felipe A
Аннотация
OBJECTIVE: To characterize the 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.
DESIGN: A 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 were followed up through 2015-2018.
METHODS: Fundus photographs analyzed with an 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.
MAIN OUTCOME MEASURES: 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.
RESULTS: Predicted RNFL loss was faster in glaucomatous vs. nonglaucomatous 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 (hazard ratio = 1.125 per 1 μm/year increase; 95% confidence interval, 1.070-1.183; P < 0.001).
CONCLUSIONS: Deep learning (DL)-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 DL models to enable glaucoma risk stratification in settings, where OCT is impractical or unavailable.
FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
DESIGN: A 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 were followed up through 2015-2018.
METHODS: Fundus photographs analyzed with an 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.
MAIN OUTCOME MEASURES: 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.
RESULTS: Predicted RNFL loss was faster in glaucomatous vs. nonglaucomatous 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 (hazard ratio = 1.125 per 1 μm/year increase; 95% confidence interval, 1.070-1.183; P < 0.001).
CONCLUSIONS: Deep learning (DL)-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 DL models to enable glaucoma risk stratification in settings, where OCT is impractical or unavailable.
FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.