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

Gustavo A Samico1, Douglas R da Costa2, Rafael Scherer2

  • 1Bascom Palmer Eye Institute, University of Miami, Miami, Florida; Department of Ophthalmology and Visual Sciences, Escola Paulista de Medicina Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil.

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|March 20, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models analyzing fundus photos can predict retinal nerve fiber layer (RNFL) thinning. Faster RNFL loss identified individuals at higher risk for developing glaucoma.

Keywords:
GlaucomaMachine-to-machinePopulation-based cohort studyRetinal nerve fiber layer

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Early detection and monitoring of glaucoma progression are crucial.
  • Retinal nerve fiber layer (RNFL) thinning is a key indicator of glaucomatous damage.

Purpose of the Study:

  • To assess the ability of a machine-to-machine (M2M) model using fundus photographs to predict longitudinal RNFL thinning.
  • To investigate the association between predicted RNFL thinning and the incidence of glaucoma in a large population-based cohort.

Main Methods:

  • A prospective, population-based cohort study involving 18,247 participants (aged 45-86) from the Canadian Longitudinal Study on Aging.
  • Fundus photographs were analyzed using an optical coherence tomography (OCT)-trained M2M algorithm to estimate RNFL thickness change over a 3-year follow-up.
  • Linear mixed-effects and Cox proportional hazards models were used to identify predictors of RNFL thinning and assess the risk of incident glaucoma.

Main Results:

  • Faster predicted RNFL thinning was observed in eyes with glaucoma compared to non-glaucomatous eyes.
  • Predictors of faster RNFL thinning included older age, higher intraocular pressure (IOP), lower corneal hysteresis (CH), and thicker baseline predicted-RNFL.
  • Faster predicted RNFL loss was independently associated with an increased risk of developing incident glaucoma (HR = 1.125 per 1 μm/year increase).

Conclusions:

  • Deep learning-derived RNFL estimates from fundus photographs accurately reflect structural changes.
  • These models show potential for predicting incident glaucoma in population-based settings.
  • Fundus-based deep learning offers a promising tool for glaucoma risk stratification, especially where OCT is unavailable.