Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT
This addresses the problem of costly genetic analysis for AMD patients by providing an AI-assisted method, though it appears incremental as it applies existing multi-modal techniques to a specific medical context.
The paper tackled predicting susceptibility genes for Age-related Macular Degeneration (AMD) by integrating fundus and OCT images with medical records, achieving over 80% accuracy in predictions.
In recent years, there has been significant development in the analysis of medical data using machine learning. It is believed that the onset of Age-related Macular Degeneration (AMD) is associated with genetic polymorphisms. However, genetic analysis is costly, and artificial intelligence may offer assistance. This paper presents a method that predict the presence of multiple susceptibility genes for AMD using fundus and Optical Coherence Tomography (OCT) images, as well as medical records. Experimental results demonstrate that integrating information from multiple modalities can effectively predict the presence of susceptibility genes with over 80$\%$ accuracy.