Journal Articles

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature. Circulation. 2022; 145(2):134-150

Structure-Function Mapping Using a Three-Dimensional Neuroretinal Rim Parameter Derived From Spectral Domain Optical Coherence Tomography Volume Scans. Transl Vis Sci Technol. 2021; 10(6):28

Age, Gender, and Laterality of Retinal Vascular Occlusion: A Retrospective Study from the IRIS® Registry. Ophthalmol Retina. 2021;

Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression. Transl Vis Sci Technol. 2021; 10(7):27

The Effect of Ametropia on Glaucomatous Visual Field Loss. J Clin Med. 2021; 10(13)

Chemical and thermal ocular burns in the United States: An IRIS registry analysis. Ocul Surf. 2021; 21:345-347

Variability and Power to Detect Progression of Different Visual Field Patterns. Ophthalmol Glaucoma. 2021; 4(6):617-623

Trends and Usage Patterns of Minimally Invasive Glaucoma Surgery in the United States: IRIS® Registry Analysis 2013-2018. Ophthalmol Glaucoma. 2021; 4(6):558-568

Association Between Diabetes, Diabetic Retinopathy, and Glaucoma. Curr Diab Rep. 2021; 21(10):38

Renal function and lipid metabolism are major predictors of circumpapillary retinal nerve fiber layer thickness-the LIFE-Adult Study. BMC Med. 2021; 19(1):202

Characteristics of p.Gln368Ter Myocilin Variant and Influence of Polygenic Risk on Glaucoma Penetrance in the UK Biobank. Ophthalmology. 2021; 128(9):1300-1311

Wide-field swept-source optical coherence tomography angiography in the assessment of retinal microvasculature and choroidal thickness in patients with myopia. Br J Ophthalmol. 2021;

Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension. Transl Vis Sci Technol. 2021; 10(9):37

Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence. Transl Vis Sci Technol. 2021; 10(9):16

Usage Patterns of Minimally Invasive Glaucoma Surgery (MIGS) Differ by Glaucoma Type: IRIS Registry Analysis 2013-2018. Ophthalmic Epidemiol. 2021; :1-9

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning. PLoS One. 2021; 16(4):e0249856

Predicting Global Test-Retest Variability of Visual Fields in Glaucoma. Ophthalmol Glaucoma. 2021.

Three-dimensional Neuroretinal Rim Thickness and Visual Fields in Glaucoma: A Broken-stick Model. J Glaucoma. 2020 Oct; 29(10):952-963

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology. 2020 Sep; 127(9):1170-1178

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma. Transl Vis Sci Technol. 2020 Aug; 9(9):41

Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med Genomics. 2020 Aug 26; 13(1):120

Norms of Interocular Circumpapillary Retinal Nerve Fiber Layer Thickness Differences at 768 Retinal Locations. Transl Vis Sci Technol. 2020 Aug 12; 9(9):23

Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility. ransl Vis Sci Technol. 2020; 9(12):22

Baseline Age and Mean Deviation Affect the Rate of Glaucomatous Vision Loss. Journal of Glaucoma. 2020. 29: 31-38.

Sex-Specific Differences in Circumpapillary Retinal Nerve Fiber Layer Thickness. Ophthalmology. 2020. 127: 357-368.

Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence. JAMA Ophthalmology. 2020. 138(2):190-198

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma. Ophthalmology. 2020. 127: 731-738.

The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye, 2020. 34(1): p. 1-11.

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis. Investigative Opthalmology & Visual Science. 2019. 60: 365.

Machine Learning in the Detection of the Glaucomatous Disc and Visual Field. Seminars in Ophthalmology. 2019. 34: 232-242.

Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms. Ophthalmology. 2019. 126: 822-828.

The Interrelationship between Refractive Error, Blood Vessel Anatomy, and Glaucomatous Visual Field Loss. Translational Vision Science & Technology. 2018. 7: 4.

Quantifying positional variation of retinal blood vessels in glaucoma. PLOS ONE. 2018. 13: e0193555.

Predicting Refractive Outcome of Small Incision Lenticule Extraction for Myopia Using Corneal Properties. Translational Vision Science & Technology. 2018. 7: 11.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma. Ophthalmology. 2018. 125: 352-360.

Systemic and Ocular Determinants of Peripapillary Retinal Nerve Fiber Layer Thickness Measurements in the European Eye Epidemiology (E3) Population. Ophthalmology. 2018. 125: 1526-1536.

New Precision Metrics for Contrast Sensitivity Testing. EEE Journal of Biomedical and Health Informatics. 2018. 22: 919-925.

Relationship Between Central Retinal Vessel Trunk Location and Visual Field Loss in Glaucoma. American Journal of Ophthalmology. 2017. 176: 53-60.

Impact of Natural Blind Spot Location on Perimetry. Scientific Reports. 2017. 7: 6143.

Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness. ournal of Biomedical Optics. 2017. 22: 1.

Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 1. Impacts of refractive error and interartery angle. Journal of Biomedical Optics. 2017. 22: 1.

Associations between Optic Nerve Head–Related Anatomical Parameters and Refractive Error over the Full Range of Glaucoma Severity. Translational Vision Science & Technology. 2017. 6: 9.

Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 2. Impacts of optic nerve head parameters. Journal of Biomedical Optics. 2017. 22: 1.

Evaluation of the precision of contrast sensitivity function assessment on a tablet device. Scientific Reports. 2017. 7: 46706.

Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic. Current Eye Research. 2017. 42: 568-574.

An evaluation of organic light emitting diode monitors for medical applications: Great timing, but luminance artifacts. Medical Physics. 2013. 40: 092701.

Patterns of functional vision loss in glaucoma determined with archetypal analysis. Journal of The Royal Society Interface. 2015. 12: 20141118.

Patterns of Retinal Nerve Fiber Layer Loss in Different Subtypes of Open Angle Glaucoma Using Spectral Domain Optical Coherence Tomography. Journal of Glaucoma. 2016. 25: 865-872.

Liquid crystal display response time estimation for medical applications. Medical Physics. 2009. 36: 4984-4990.

Achieving precise display timing in visual neuroscience experiments. Journal of Neuroscience Methods. 2010. 191: 171-179.

Misspecifications of Stimulus Presentation Durations in Experimental Psychology: A Systematic Review of the Psychophysics Literature. PLoS ONE. 2010. 5: e12792.

The Early Time Course of Compensatory Face Processing in Congenital Prosopagnosia. PLoS ONE. 2010. 5: e11482.

Chinese characters reveal impacts of prior experience on very early stages of perception. BMC Neuroscience. 2011. 12: 14.

Deficits in Long-Term Recognition Memory Reveal Dissociated Subtypes in Congenital Prosopagnosia. PLoS ONE. 2011. 6: e15702.

A computational model of dysfunctional facial encoding in congenital prosopagnosia. Neural Networks. 2011. 24: 652-664.

Temporal Properties of Liquid Crystal Displays: Implications for Vision Science Experiments. PLoS ONE. 2012. 7: e44048.

A predictive approach to nonparametric inference for adaptive sequential sampling of psychophysical experiments. ournal of Mathematical Psychology. 2012. 56: 179-195.