A Novel Approach to Personalized Prediction of Progression of Age-Related Macular Degeneration
The majority of patients with advanced AMD have severe vision loss. Despite the development of artificial intelligence algorithms for personalized AMD progression, we are still far from implementing tailored follow-up care and treatments for AMD patients. Our hypothesis is that the probability of AMD progression can be predicted by integrating imaging, genetic and clinical data in statistical predictive models, thereby improving personalized care.
The goal of our study is to predict the progression of age-related macular degeneration (AMD). We will first expand and validate our fully-automated image processing algorithms in predicting future choroidal neovascularization and geographic atrophy events to include genetic data and real-world longitudinal patient data. In our second aim, we will initiate a randomized, controlled clinical trial to test the viability of applying personalized AMD prediction models. This study integrates imaging, genetic, demographic, and clinical patient parameters from several data sources, with an existing pipeline for image feature extraction. This innovative approach will provide time-dependent probabilities for predicting advanced AMD. Testing algorithms in a clinical trial setting is another novel aspect of this research.
Our work will advance the AMD field by improving the identification of high-risk patients as candidates for more frequent screening and earlier treatment, leading to better clinical outcomes. Results may lead to developing a tool to predict the chances of AMD progression on a personalized, patient-by-patient basis.
About the Researcher
Dr. Joelle Hallak is Assistant Professor of Ophthalmology and the Executive Director of the Ophthalmic Clinical Trials and Translational Research Center, at the Department of Ophthalmology and Visual Sciences, the University of Illinois at Chicago. Her doctoral training was in Epidemiology and Biostatistics, with specific expertise in ophthalmic epidemiology and statistical learning research, biological and genetic markers, comparative effectiveness research and analysis of complex big data. She was also a Visiting Scholar at Stanford University, where she has developed skills and collaborations in computational applications. She is currently investigating new methods to integrate patient data for predicting disease progression in AMD and other blinding diseases.
I am extremely honored to be part of the BrightFocus 2019 Awardees. My interest in blinding eye diseases arose after observing the debilitation on affected patients. With my background in statistical and epidemiological research and the rise of big data applications, I realized that with the proper tools we will be able to improve the diagnosis of AMD as well as the early identification of patients who are likely to progress to a blinding stage. Our work, funded by BrightFocus, is committed to making that happen, and our methods may lead to a tool that provides better identification of high-risk patients in AMD, who should have more intensive screening, detecting progression and enabling treatment at an earlier stage, and ultimately resulting in better clinical outcomes.
First published on: July 2, 2019
Last modified on: July 3, 2019