AI 中文总结
本研究提出基于JEPA架构的LeDXA模型，利用少量数据和计算从DXA图像中学习到可编码多系统疾病风险、生物衰老与遗传率的表征，提升了疾病预测及相关生物指标的表现。
AI 中文摘要
全身双能X线吸收测定法（DXA）扫描常规用于测量骨密度和局部身体成分，但其空间结构大多未被利用。本文展示自监督学习（SSL）可将原始DXA图像转换为全身健康的表征，我们推出LeDXA，一种基于联合嵌入预测架构（JEPA）的视觉模型，该模型通过预测潜在表征而非重建像素进行学习。LeDXA在11540张未标注的人类表型项目（Human Phenotype Project）扫描数据上从头训练，经内部评估及47400张外部英国生物银行（UKBB）扫描数据验证，尽管训练图像数量比DINOv3（一种通用型最先进模型）少约15万倍、参数少近40倍，其对常见疾病和生物标志物的跨队列预测仍优于扫描仪衍生的DXA测量值与DINOv3。在UKBB中位随访4.3年期间，LeDXA对发病疾病的预测优于表格型DXA指标，对髋膝关节炎和2型糖尿病的提升最为显著；其中髋关节炎的发病病例中，66%出现在最高风险四分位组，而表格型指标仅为41%。其表征可在外部数据中预测实际年龄（相关系数r=0.88，平均绝对误差=2.90年），生物年龄差距与更广泛的疾病负担相关，且在外观最显衰老的四分位组中，该差距对应45%的更高死亡风险；女性开始接受激素替代疗法后，该差距缩小，表明其具有可修饰性。全基因组关联分析主要恢复了已知的身体成分和骨密度位点，且LeDXA嵌入的可遗传性高于DINOv3。这些发现揭示了DXA图像中存在常规判读所丢弃的预后信息，且可通过相对少量数据和适度计算学习获得。
英文摘要
Whole-body dual-energy X-ray absorptiometry (DXA) scans are routinely acquired to measure bone density and regional body composition, leaving their spatial structure largely unused. Here, we show that self-supervised learning (SSL) can convert raw DXA images into representations of systemic health. We introduce LeDXA, a vision model based on a joint-embedding predictive architecture (JEPA) that learns by predicting latent representations rather than reconstructing pixels. Trained from scratch on 11,540 unlabeled Human Phenotype Project scans, LeDXA was evaluated internally and on 47,400 external UK Biobank (UKBB) scans. It improved cross-cohort prediction of prevalent diseases and biomarkers beyond scanner-derived DXA measurements and DINOv3, a state-of-the-art general-purpose model, despite approximately 150,000-fold fewer training images and nearly 40-fold fewer parameters. Over a median 4.3-year UKBB follow-up, LeDXA improved incident disease prediction over tabular DXA measures, with the largest gains for hip and knee arthrosis and type 2 diabetes. For hip arthrosis, 66% of incident cases occurred in the highest-risk quartile versus 41% for tabular measures. Its representations predicted chronological age externally (r = 0.88; mean absolute error = 2.90 years), and the biological-age gap tracked broader disease burden and a 45% higher mortality hazard in the oldest-appearing quartile. The gap also decreased in women after starting hormone-replacement therapy, suggesting it may be modifiable. Genome-wide associations recovered mostly known body-composition and bone-density loci, and LeDXA embeddings were more heritable than DINOv3's. These findings reveal prognostic information in DXA images that conventional readouts discard, learnable with relatively little data and modest compute.
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