mengyu_wang@meei.harvard.edu
For investigator inquiries only
About My Research
Center/Research Area Affiliations
Biography
Dr. Mengyu Wang is Co-Director of the Harvard Ophthalmology Artificial Intelligence Lab. Dr. Wang is a multidisciplinary researcher with extensive training in the areas of ophthalmology, radiology, artificial intelligence, medical image analysis, and computational mechanics. Dr. Wang’s current research focuses on developing mathematical models to better understand eye diseases by using the techniques of artificial intelligence, advanced statistics, image processing, and biomechanics to eventually improve the diagnosis, prognosis, and treatment of eye diseases to ultimately save sight.
Dr. Wang obtained his PhD in Structural Engineering and Mechanics from the University of Pittsburgh in 2014. For his PhD research, Dr. Wang developed various computational approaches to inversely characterize structural properties with applications in civil structure, tumor and artificial blood vessel with combined techniques of finite element modeling, optimization and artificial intelligence. After his PhD study, Dr. Wang went on for a postdoctoral fellowship with a research focus on medical image analysis and artificial intelligence modeling in the Department of Radiology at Duke University for a year. In 2015, he moved to Schepens Eye Research Institute of Massachusetts Eye and Ear and Harvard Medical School as a postdoctoral fellow to develop statistical models and artificial intelligence based methods to improve glaucoma diagnosis and monitoring. In 2017, he was promoted to the rank of Instructor. In 2019, Dr. Wang received a prestigious NIH K99/R00 Pathway to Independence Award for his study on relationship between glaucoma and the three-dimensional optic nerve head related structure. In 2020, Dr. Wang was promoted to Assistant Professor at Schepens Eye Research Institute of Massachusetts Eye and Ear and Harvard Medical School.
Dr. Wang is a co-inventor on a number of patents leveraging artificial intelligence techniques to improve the diagnosis and prognosis of eye diseases.
Websites
Education
2014: PhD, Structural Engineering and Mechanics, University of Pittsburgh
Postgraduate Training
2014-2015: Postdoctoral Associate, Breast Cancer Imaging, Department of Radiology, Duke University
2015-2017: Postdoctoral Fellow, Ophthalmology, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School
Academic Appointments
2017-2020: Instructor in Ophthalmology, Harvard Medical School
2020-Present: Assistant Professor of Ophthalmology, Harvard Medical School
Professional Memberships
2015-Present: Association for Research in Vision and Ophthalmology
Honors
2018: Fellow’s Best Scientific Paper of 2017-2018, Massachusetts Eye and Ear
2019: Murray and Jeanie Johnstone Travel Grant, Association for Research in Vision and Ophthalmology
2019: K99/R00 Pathway to Independence Award, National Institutes of Health
2020: Caring for Dependents Travel Award, Massachusetts General Hospital
- Combined Model of OCT Angiography and Structural OCT Parameters to Predict Paracentral Visual Field Loss in Primary Open-Angle Glaucoma. Ophthalmol Glaucoma. 2023 May-Jun; 6(3):255-265.
- Structure-function association between contrast sensitivity and retinal thickness (total, regional, and individual retinal layer) in patients with idiopathic epiretinal membrane. Graefes Arch Clin Exp Ophthalmol. 2023 Mar; 261(3):631-639.
- Cohort Study of Race/Ethnicity and Incident Primary Open-Angle Glaucoma Characterized by Autonomously Determined Visual Field Loss Patterns. Transl Vis Sci Technol. 2022 07 08; 11(7):21.
- An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence. J Glaucoma. 2022 08 01; 31(8):626-633.
- Race and Ethnicity Differences in Disease Severity and Visual Field Progression Among Glaucoma Patients. Am J Ophthalmol. 2022 10; 242:69-76.
- Assessing Surface Shapes of the Optic Nerve Head and Peripapillary Retinal Nerve Fiber Layer in Glaucoma with Artificial Intelligence. Ophthalmol Sci. 2022 Sep; 2(3):100161.
- Bevacizumab in High-Risk Corneal Transplantation: A Pilot Multicenter Prospective Randomized Control Trial. Ophthalmology. 2022 08; 129(8):865-879.
- Reading cognition from the eyes: association of retinal nerve fibre layer thickness with cognitive performance in a population-based study. Brain Commun. 2021; 3(4):fcab258.
- Renal function and lipid metabolism are major predictors of circumpapillary retinal nerve fiber layer thickness-the LIFE-Adult Study. BMC Med. 2021 09 07; 19(1):202.
- Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence. Transl Vis Sci Technol. 2021 08 02; 10(9):16.
- The Effect of Ametropia on Glaucomatous Visual Field Loss. J Clin Med. 2021 Jun 25; 10(13).
- Paired Optic Nerve Microvasculature and Nailfold Capillary Measurements in Primary Open-Angle Glaucoma. Transl Vis Sci Technol. 2021 06 01; 10(7):13.
- Predicting Global Test-Retest Variability of Visual Fields in Glaucoma. Ophthalmol Glaucoma. 2021 Jul-Aug; 4(4):390-399.
- Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility. Transl Vis Sci Technol. 2020 11; 9(12):22.
- Characterization of Prelaminar Wedge-Shaped Defects in Primary Open-Angle Glaucoma. Curr Eye Res. 2021 06; 46(6):895-902.
- Quantification of the Peripapillary Microvasculature in Eyes with Glaucomatous Paracentral Visual Field Loss. Ophthalmol Glaucoma. 2021 May-Jun; 4(3):286-294.
- An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma. Transl Vis Sci Technol. 2020 08; 9(9):41.
- Norms of Interocular Circumpapillary Retinal Nerve Fiber Layer Thickness Differences at 768 Retinal Locations. Transl Vis Sci Technol. 2020 08; 9(9):23.
- Prevalence of Persistent Corneal Epithelial Defects in Chronic Ocular Graft-Versus-Host Disease. Am J Ophthalmol. 2020 10; 218:296-303.
- Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology. 2020 09; 127(9):1170-1178.
- Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence. JAMA Ophthalmol. 2020 02 01; 138(2):190-198.
- Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma. Ophthalmology. 2020 06; 127(6):731-738.
- Sex-Specific Differences in Circumpapillary Retinal Nerve Fiber Layer Thickness. Ophthalmology. 2020 03; 127(3):357-368.
- The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye (Lond). 2020 01; 34(1):1-11.
- Microvasculature of the Optic Nerve Head and Peripapillary Region in Patients With Primary Open-Angle Glaucoma. J Glaucoma. 2019 04; 28(4):281-288.
- An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis. Invest Ophthalmol Vis Sci. 2019 01 02; 60(1):365-375.
- Predicting Refractive Outcome of Small Incision Lenticule Extraction for Myopia Using Corneal Properties. Transl Vis Sci Technol. 2018 Sep; 7(5):11.
- Quantitative analysis of optical coherence tomographic angiography (OCT-A) in patients with non-arteritic anterior ischemic optic neuropathy (NAION) corresponds to visual function. PLoS One. 2018; 13(6):e0199793.
- Quantifying positional variation of retinal blood vessels in glaucoma. PLoS One. 2018; 13(3):e0193555.
- The Interrelationship between Refractive Error, Blood Vessel Anatomy, and Glaucomatous Visual Field Loss. Transl Vis Sci Technol. 2018 Jan; 7(1):4.
- Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness. J Biomed Opt. 2017 12; 22(12):1-19.
- Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 2. Impacts of optic nerve head parameters. J Biomed Opt. 2017 Dec; 22(12):1-9.
- Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 1. Impacts of refractive error and interartery angle. J Biomed Opt. 2017 Dec; 22(12):1-11.
- Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma. Ophthalmology. 2018 03; 125(3):352-360.
- Impact of Natural Blind Spot Location on Perimetry. Sci Rep. 2017 07 21; 7(1):6143.
- Associations between Optic Nerve Head-Related Anatomical Parameters and Refractive Error over the Full Range of Glaucoma Severity. Transl Vis Sci Technol. 2017 Jul; 6(4):9.
- Relationship Between Central Retinal Vessel Trunk Location and Visual Field Loss in Glaucoma. Am J Ophthalmol. 2017 Apr; 176:53-60.
- A Generalized Computationally Efficient Inverse Characterization Approach Combining Direct Inversion Solution Initialization with Gradient-Based Optimization. Computational Mechanics. 2016; 59:507–521.
- A Computer Vision-Based Algorithm to Predict False Positive Errors in Radiology Trainees when Interpreting Digital Breast Tomosynthesis Cases. Expert Systems with Applications. 2016; 64:490-499.
- Predicting False Negative Errors in Digital Breast Tomosynthesis among Radiology Trainees Using a Computer Vision-Based Approach. Expert Systems with Applications. 2016; 56:1-8.
- A Computationally Efficient Approach for Inverse Material Characterization Combining Gappy POD with Direct Inversion. Computer Methods in Applied Mechanics and Engineering. 2015; 286:373-393.
- A Computational Nondestructive Evaluation Algorithm Combining Self-Evolving Parameterization and Multi-Objective Optimization for Quantitative Damage Characterization. Journal of Nondestructive Evaluation. 2014; 33(4):547-561.
- Assessment of Multi-Objective Optimization for Nondestructive Evaluation of Damage in Structural Components. Journal of Intelligent Material Systems and Structures. 2014; 25(9):1082–1096.
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