用皮肤活检图像和对比学习，发现可预测寿命的衰老生物标志物。
Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies
- 通过对比学习分析皮肤活检图像，提取衰老相关视觉特征。
- 该特征能有效预测丹麦人群的死亡率与慢性病发病率。
- 为临床常规数据融合AI提供新范式，适合老龄化研究者参考。
随着全球预期寿命增加，慢性病负担加重，但个体衰老速度差异显著。识别快慢衰老的生物标志物对理解衰老机制、实现疾病早期检测和优化预防策略至关重要。本研究利用对比深度学习方法，证明仅凭皮肤活检图像即可推断个体年龄。进一步基于组织病理切片中的视觉特征，构建了一种新型衰老生物标志物。结合丹麦全面的健康登记数据，我们验证了这些视觉特征能有效预测死亡率及慢性衰老相关疾病的发生率。研究表明，将常规收集的医疗数据与深度学习结合，可创造具有实际应用价值的衰老生物标志物，用于长期寿命预测。
原文摘要 · Abstract (English)
As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time.
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