Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT

TL;DR

This paper introduces a multi-modal deep learning approach using Selective Vision Transformer to predict genetic susceptibility to AMD from fundus images, OCT scans, and medical records, achieving over 80% accuracy.

Contribution

It presents a novel multi-modal AI model that combines fundus, OCT, and medical data for genetic prediction of AMD, improving accuracy and efficiency.

Findings

Over 80% accuracy in predicting susceptibility genes

Effective integration of multi-modal medical data

Potential for cost reduction in genetic analysis

Abstract

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.