Malek Davina A

Short-term Detection of Fast Progressors in Glaucoma: The Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study.

Clustered testing in the Fast-PACE Study detected fast-progressing glaucoma eyes over 6 months.

Longitudinal Simulated Driving Performance and Rates of Progressive Visual Field Loss in Glaucoma.

Faster VF progression in patients with glaucoma was associated with worsening performance on a divided attention task during driving simulation.

RNFL Thickness in a Population-Based Cohort: The Canadian Longitudinal Study on Aging M2M (Machine-to-Machine) Study.

The M2M model provided robust estimates of RNFL thickness from fundus photographs in a large population-based cohort.

Intraocular Pressure Measurements With the Tono-Vera: A New Handheld Rebound Tonometer.

There was a strong correlation and agreement in IOP measurements between calibrated Tono-Vera IOP and ORA IOPg.

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

Deep learning-predicted retinal nerve fiber layer loss from fundus photos correlates with meaningful structural change and predicts incident glaucoma. This offers a valuable tool for glaucoma risk stratification, particularly where OCT is unavailable.