Artificial Intelligence-Based Predictions in Neovascular Age-Related Macular Degeneration

Daniela Ferrara; Elizabeth M. Newton; Aaron Y. Lee


Curr Opin Ophthalmol. 2021;32(5):389-396. 

In This Article


Artificial intelligence-based models can potentially improve clinical research and clinical practice in nAMD, enabling best visual outcomes with least treatment burden for each individual patient. Artificial intelligence has facilitated analysis of OCT and multimodal image datasets on a scale not previously possible, furthering knowledge of the disease and response to treatment. Furthermore, artificial intelligence has a number of applications to clinical trial design, implementation, and analysis, which could improve the process of clinical development at all stages and improve confidence in decision making, particularly for early-stage clinical trials. Artificial intelligence also has the potential to create powerful tools to inform point-of-care treatment decisions in a treatment landscape for nAMD of increasing complexity. However, a large gap remains between application of artificial intelligence to research and application to treatment decisions in clinical practice. A key limitation toward this goal is the shortage of large, robust datasets that represent the heterogeneity of the patients, their disease, and treatment response.