Area of research
Ophthalmology · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Glaucoma and retinal disorders, Retinal Diseases and Treatments, Retinal Imaging and Analysis, and Ophthalmology and Visual Impairment Studies.
Risk of Falls, Fear of Falling, and Rates of Visual Field Progression in Glaucoma in the African Descent and Glaucoma Evaluation Study
Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data
Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data
Fast Progressors in Glaucoma
Racial Differences in Diagnostic Accuracy of Retinal Nerve Fiber Layer Thickness in Primary Open-Angle Glaucoma
Glaucomatous Visual Field Progression in the African Descent and Glaucoma Evaluation Study (ADAGES): Eleven Years of Follow-up
Identifying and understanding optical coherence tomography artifacts that may be confused with glaucoma
Nicotinamide and Pyruvate for Neuroenhancement in Open-Angle Glaucoma
Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning
Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Characteristics of Central Visual Field Progression in Eyes with Optic Disc Hemorrhage
Manhattan Vision Screening and Follow-Up Study in Vulnerable Populations: 1-Month Feasibility Results.
Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence
Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence–Enabled Dashboard
Review of Hygiene and Disinfection Recommendations for Outpatient Glaucoma Care: A COVID Era Update
Predicting Global Test–Retest Variability of Visual Fields in Glaucoma
Disc Hemorrhages Are Associated With the Presence and Progression of Glaucomatous Central Visual Field Defects
Detection of Progression With 10-2 Standard Automated Perimetry: Development and Validation of an Event-Based Algorithm
Central Visual Field Defects in Patients with Distinct Glaucomatous Optic Disc Phenotypes
An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis
Review of the measurement and management of 24-hour intraocular pressure in patients with glaucoma
Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma
Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms
Effect of the level of effort during resistance training on intraocular pressure.
Baseline Age and Mean Deviation Affect the Rate of Glaucomatous Vision Loss
Baseline 24-2 Central Visual Field Damage Is Predictive of Global Progressive Field Loss
Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects
24-2 Visual Fields Miss Central Defects Shown on 10-2 Tests in Glaucoma Suspects, Ocular Hypertensives, and Early Glaucoma
Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma
Measuring Rates of Visual Field Progression in Linear Versus Nonlinear Scales: Implications for Understanding the Relationship Between Baseline Damage and Target Rates of Glaucoma Progression