用眼底和OCT影像+病历预测老年黄斑变性的易感基因，准确率超80%。
Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT
- 融合眼底图像、OCT和病历数据，用多模态选择性ViT建模
- 在多个基因位点上预测准确率超过80%
- 为低成本遗传风险筛查提供新思路，适合眼科与AI交叉研究者
近年来，机器学习在医学数据分析中取得显著进展。研究表明，老年黄斑变性（AMD）的发生与遗传多态性相关。然而，遗传分析成本较高，人工智能可能提供辅助。本文提出一种方法，利用眼底图像、光学相干断层扫描（OCT）图像及医疗记录，预测AMD多个易感基因的存在。实验结果表明，多模态信息融合可有效预测易感基因，准确率超过80%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。