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English(EN) Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

深度学习从视网膜图像预测阿尔茨海默病风险因素

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了能够从视网膜图像预测 12 种阿尔茨海默病风险因素的深度学习模型。这些模型在英国生物银行超过 62,000 张图像上进行训练，分析了视神经盘和血管等视网膜结构。研究发现，这些深度学习衍生的视网膜表征在后来患上阿尔茨海默病的人与匹配的对照组之间显示出显著差异，表明视网膜变化与阿尔茨海默病前期的易感性之间存在关联。 AI

影响 展示了通过非侵入性人工智能驱动的方法识别神经退行性疾病风险因素的潜力。

排序理由 学术论文，详细介绍了深度学习在医学风险因素预测方面的新应用。

在 arXiv cs.CV 阅读 →

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深度学习从视网膜图像预测阿尔茨海默病风险因素

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Seowung Leem, Yunchao Yang, Adam J. Woods, Ruogu Fang ·

    Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

    arXiv:2605.00665v1 Announce Type: new Abstract: The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker studies. Whether colored fundus photography (CFP) contains retinal structural sig…

  2. arXiv cs.CV TIER_1 English(EN) · Ruogu Fang ·

    Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

    The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker studies. Whether colored fundus photography (CFP) contains retinal structural signatures corresponding to these AD-related risk d…

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