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New research · Ophthalmology
Ophthalmology science · 23h
AI / informaticsOphthalmology science · 2026

Machine Learning to Predict Outcomes of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration.

David M Wright, Ilanit Trifonov, Marganit Shahar-Gonen … Tunde Peto
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OphthalmologyAI / informatics

Machine learning accurately predicts anti-VEGF injection burden in neovascular age-related macular degeneration.

Machine Learning to Predict Outcomes of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration.

David M Wright et al. · Ophthalmology science · 2026
Purpose

To develop machine learning models using OCT fluid metrics to predict long-term anti-VEGF treatment intensity and visual acuity (VA) outcomes in 2 large clinical data sets of patients with neovascular age-related macular degeneration (nAMD).

Methods

Retrospective, multicenter cohort study.

n = 2922 eyes
Results

nearly all eye injection predictions were within 2 shots of actual at year 1

Year 1
99.6%
Year 2
95.5%
Year 3
97.1%
More results

Visual acuity predictions were within ≤0.2 logarithm of the minimum angle of resolution units (≤2 ETDRS lines) for 92.5%, 73.5%, and 76.5% of eyes at the corresponding time points.

In the external cohort, model performance was reduced, with 45.0% to 53.8% of injection predictions and 26.3% to 41.1% of VA predictions meeting accuracy thresholds.

More results

Subretinal fluid at 6 months was the primary driver of injection burden predictions, whereas intraretinal fluid was most influential for VA outcomes.

“
Conclusion · 1 of 2

Machine learning models based on automated fluid metrics can accurately predict treatment burden and VA outcomes within the clinical environment in which they are developed.

Conclusion · 2 of 2

Although performance declined with external validation, reflecting differences in treatment policies and health care systems, locally trained models remain clinically valuable.

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