Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910607471542272 |
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| author | Furukawa, Yoichi Kamiya, Satoshi Sakurada, Yoichi Kashiwagi, Kenji Hotta, Kazuhiro |
| author_facet | Furukawa, Yoichi Kamiya, Satoshi Sakurada, Yoichi Kashiwagi, Kenji Hotta, Kazuhiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT Furukawa, Yoichi Kamiya, Satoshi Sakurada, Yoichi Kashiwagi, Kenji Hotta, Kazuhiro Computer Vision and Pattern Recognition 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. |
| title | Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.11128 |