Konstantinos Balaskas
Papers
1
Total Citations
13
H-Index
1
About
Konstantinos Balaskas is a leading clinician-scientist whose work sits at the intersection of ophthalmology and artificial intelligence, with a primary focus on age-related macular degeneration (AMD). His research is pioneering the use of machine learning to analyze sparse optical coherence tomography (OCT) data, demonstrating that even small, clinically realistic datasets can power robust diagnostic algorithms. In his landmark 2019 study, Balaskas showed the feasibility of a support vector machine (SVM) learning model for automatically monitoring neovascular (wet) AMD, a breakthrough that promises to reduce the burden of frequent imaging on patients and healthcare systems. This work, which has garnered over a dozen citations, underscores his commitment to translating computational methods into practical, scalable tools for retinal disease management. Beyond this, Balaskas has made significant contributions to understanding the pathophysiology of AMD and optimizing treatment protocols. His research is widely recognized for bridging the gap between cutting-edge AI and real-world clinical application, making him a key figure in the future of precision ophthalmology.
Research Focus
Key Achievements
Top Papers
- 1