Catherine Egan
Papers
1
Total Citations
13
H-Index
1
About
Catherine Egan is a leading clinician-scientist whose research centers on the application of artificial intelligence and advanced imaging to retinal disease, particularly age-related macular degeneration (AMD). Her major contribution lies in pioneering the use of machine learning to analyze sparse optical coherence tomography (OCT) data, demonstrating that even small, clinically realistic datasets can yield powerful diagnostic insights. In her highly cited 2019 study, she showed the feasibility of a support vector machine learning algorithm for automatically monitoring neovascular (wet) AMD, a breakthrough that promises to reduce the burden of frequent clinic visits and enable more personalized, data-driven treatment decisions. This work has garnered 13 citations and established a new paradigm for integrating AI into routine ophthalmic care. Dr. Egan’s research is notable for its practical focus: she bridges the gap between complex computational methods and real-world clinical workflows, making her a key figure in the move toward automated, accessible retinal disease management. Her ongoing efforts continue to shape how ophthalmologists leverage imaging data to improve patient outcomes.
Research Focus
Key Achievements
Top Papers
- 1