Amitha Domalpally, MD, PhD

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Appointments and Honors

Research Director of the Wisconsin Reading Center

Research Interests

• Discovery, development and translation of imaging biomarkers for clinical trials in retinal diseases
• Artificial Intelligence for retinal imaging
• Clinical Trials in diabetic retinopathy, AMD, retinal vein occlusion and uveitis
• Imaging artifacts

Research Highlights

Dr. Domalpally is the Research Director of the Wisconsin Reading Center, one of the world’s leading diagnostic imaging centers for both patient diagnosis and clinical trials. Her work investigates clinical trial imaging endpoints for retinal diseases, with a focus on employing new imaging techniques to understand the natural history and prognostic markers of diseases such as age-related macular degeneration (AMD), diabetic retinopathy, and many others. In collaboration with National Eye Institute and other researchers around the nation, Dr. Domalpally led the imaging endpoints for the Age-Related Eye Disease Study 2(AREDS2), an influential study investigating the role of supplements in AREDS formulation for age-related macular degeneration (AMD) treatment. She has published over 25 papers for this study alone.
Lutein + zeaxanthin and omega-3 fatty acids for age-related macular degeneration: the Age-Related Eye Disease Study 2 (AREDS2) randomized clinical trial

Dr. Domalpally has a strong focus on improving the interpretation of the abundant and complex imaging data in clinical trials. A better understanding of potential errors in imaging technologies, for instance, reduces false interpretation of data and improves patient management. Dr. Domalpally has detailed artifacts that are intrinsic to OCT angiography technology that help clinicians quickly flag unreliable images that should be discarded.
Prevalence and severity of artifacts in optical coherence tomographic angiograms

She is also involved in developing diagnostic artificial intelligence (AI) algorithms for retinal diseases. In collaboration with the National Eye Institute, she has developed a deep learning algorithm that assists in detecting reticular pseudodrusen, a feature associated with AMD that is difficult to identify by a human observer.
Deep learning automated detection of reticular pseudodrusen from fundus autofluorescence images or color fundus photographs in AREDS2

Education

PhD: Clinical Investigation, Institute of Clinical and Transitional Research, University of Wisconsin, Madison, IW

Residency: Christian Medical College, Vellore, India

Internship: Christian Medical College, Vellore, India

Medical School: Christian Medical College, Vellore, India