用眼底照片深度学习预测阿尔茨海默病风险因素，发现视网膜结构与疾病风险密切相关。
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
- 通过深度学习从眼底图像中提取12个阿尔茨海默病风险因子的特征
- 模型在连续变量上最高解释力达R²=0.7620，部分指标可提前8.55年区分患者与健康人
- 关键区域为视神经头和视网膜血管，揭示潜在生物学关联
系统性、代谢性及生活方式因素已通过流行病学和特异性生物标志物研究证实与阿尔茨海默病（AD）相关。彩色眼底摄影（CFP）是否包含对应这些风险因素的视网膜结构特征尚不明确。本研究利用英国生物银行44,501名参与者共62,876张眼底图像，训练深度学习模型预测12项与AD发病相关的风险因素：6项分类变量（性别、吸烟、失眠、经济状况、饮酒、抑郁）和6项连续变量（年龄、受教育年限、BMI、收缩压、舒张压、HbA1c）。评估模型性能、显著性图及基于显著性的评分（CAM-Score），并与视网膜形态测量对比。结果显示，分类任务的AUROC范围为0.5654–0.9480，连续变量的R²为-0.0291–0.7620，优于多数形态测量机器学习模型。显著性图持续聚焦于视神经头和视网膜血管等生物合理区域，且与现有形态差异一致。部分评分在新发AD病例（平均距发病8.55年）与匹配对照间存在显著差异，提示风险因素的视网膜表征与早期病变存在重叠。眼底图像编码了与阿尔茨海默病风险相关的视网膜信号，虽非诊断工具，但深度学习生成的视网膜表征可能揭示反映潜在疾病易感性的生物结构变化。
原文摘要 · Abstract (English)
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 domains remains unclear. To determine whether deep learning (DL) models can predict 12 AD-related risk factors from CFP and to characterize the retinal structures underlying these predictions, thereby assessing whether CFP reflects pathways to AD vulnerability. Using 62,876 CFPs from 44,501 unique participants from the UK Biobank, DL models were trained to predict 12 factors linked to AD incidence: 6 categorical (sex, smoking, sleeplessness, economic status, alcohol use, depression) and 6 continuous (age, age at completing education, BMI, systolic, diastolic blood pressure, HbA1c). Model performance, model saliency, and saliency-derived scores (CAM-Score) were evaluated and compared to retinal morphometry. The scores were also compared between incident-AD cases (average 8.55 years before onset) and matched controls. Performance of DL ranged from AUROC= 0.5654-0.9480 for categorical and R2=-0.0291-0.7620 for continuous factors, outperforming most of the morphometry-machine learning models. Saliency-based score consistently highlighted biologically meaningful regions, particularly the optic nerve head and retinal vasculature. It also aligned with present morphometric variations. Several saliency-based scores differed significantly between incident AD and matched controls, suggesting potential overlap between retinal correlates of risk factors and preclinical AD-associated changes. CFP encodes retinal signatures linked to AD risk factors. Although not diagnostic, DL-derived retinal representations may uncover biologically meaningful risk-related structural changes mirroring the potential AD vulnerability.
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