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

Yoichi Furukawa, Satoshi Kamiya, Yoichi Sakurada, Kenji Kashiwagi, Kazuhiro Hotta
9/17/2024
cs.CV

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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.

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Cite this paper

@article{furukawa2024genetic,
  title  = {Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT},
  author = {Yoichi Furukawa and Satoshi Kamiya and Yoichi Sakurada and Kenji Kashiwagi and Kazuhiro Hotta},
  year   = {2024},
  eprint = {2409.11128},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.11128v1}
}

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