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Biography

Nazlee Zebardast, MD is focused on developing machine learning based tools for disease detection, aiding clinicians in assessing for disease progression and eventually optimizing patient-related outcomes. Currently, in collaboration with researchers at Ocular Genomics Institute and Massachusetts General Hospital she is using statistical genetics and machine learning methods for glaucoma genetic risk prediction. Specifically, her research focuses on imaging genetics with aim to 1) develop a polygenic risk score for open angle glaucoma and construct deep learning models for glaucoma genetic risk prediction using fundus imaging and 2) define structural and longitudinal endophenotypes using machine learning methods that are closely aligned with disease subtype and progression and identify novel genetic markers for disease. This work could lay the foundation for optimized machine learning based screening tests built on fundus images and/or genetic risk profiling.

In addition to this work, Dr Zebardast has an established track record in global health and epidemiology research. She is currently working with a number of large datasets including NHANES, IFACE, SEE, UK Biobank, IRIS registry, Medicare and NEISS to answer important clinical questions. Specifically, she is currently exploring the extensive IRIS registry to better understand the current use of MIGS in the US and delineate which patients may most benefit from these procedures and using Medicare claims data to identify sociodemographic predictors of outcomes, healthcare utilization and cost among older adults with glaucoma.