BioE PhD Thesis Proposal Presentation - Gabriela Sanchez Rodriguez

Advisor:

Dr. Andrew J. Feola, Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory; Department of Ophthalmology, Emory University

Committee Members:

Dr. C. Ross Ethier, Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory

Dr. Levi B. Wood, George W. Woodruff School of Mechanical Engineering, Georgia Tech

Dr. James Hays, School of Interactive Computing, Georgia Tech

Dr. Stanislav Emelianov, School of Electrical and Computer Engineering, Georgia Tech and Emory

Dr. Vicky (Thao) Nguyen, Whiting School of Engineering, Johns Hopkins University

Mechanical Characterization of the Posterior Eye with Physics-Informed Deep Learning and Transcriptomics

Glaucoma is the leading cause of irreversible blindness worldwide, and the only modifiable risk factor is elevated intraocular pressure, which is often associated with remodeling of the posterior eye. Females represent a higher proportion of glaucoma patients than males, and early menopause has emerged as a sex-specific risk factor. Preclinical studies show that surgical menopause alters the biomechanical properties of the posterior eye, yet the underlying mechanisms and longitudinal impact remain unknown, representing a significant knowledge gap. This proposal addresses this gap by 1) developing a deep learning tool to segment posterior eye structures from optical coherence tomography (OCT) images, 2) reformulating the Virtual Fields Method as a machine-learning problem to infer tissue mechanical properties, 3) extending this framework to in vivo OCT displacement data to quantify tissue mechanical properties, and 4) characterizing the transcriptomic changes induced by early menopause in the posterior eye to identify candidate genes and pathways underlying mechanical changes and the association with an increased risk of developing glaucoma. The proposed research will provide a computational framework for inferring posterior eye mechanical properties from OCT images, expand understanding of the timing and mechanisms linking early menopause to glaucoma risk, and identify candidate molecular pathways for future mechanistic and therapeutic study.