Sandrine Zweifel
Papers
1
Total Citations
13
H-Index
1
About
Sandrine Zweifel is a leading clinician-scientist in ophthalmology, recognized for pioneering the application of artificial intelligence in retinal imaging. Her core research focuses on age-related macular degeneration (AMD), particularly the use of machine learning to analyze sparse optical coherence tomography (OCT) data. In her landmark 2019 study, Zweifel demonstrated the feasibility of a support vector machine learning (SVML) algorithm for automatically monitoring neovascular (wet) AMD, achieving a breakthrough in managing patients with limited or irregular follow-up data. This work, which has garnered 13 citations, addresses a critical clinical challenge: how to leverage small, real-world datasets for accurate disease surveillance. By proving that AI can extract meaningful patterns from sparse OCT scans, she has opened new pathways for cost-effective, accessible retinal care. Her contributions are vital for students and researchers exploring the intersection of deep learning and ophthalmology, as she provides a practical framework for deploying AI in settings where traditional dense imaging is unavailable. Zweifel’s research continues to shape how clinicians harness computational tools to preserve vision in aging populations.
Research Focus
Key Achievements
Top Papers
- 1