用联邦学习预测中风患者脑龄，不共享数据也能精准评估康复风险。
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction
- 通过联邦学习在16家医院分散训练模型，避免数据集中
- 脑龄差异与糖尿病、3个月后功能恢复显著相关
- 适合关注中风预后、数据隐私敏感的临床研究者
脑龄差（BrainAGE）是反映脑健康的影像生物标志物。然而，训练鲁棒的BrainAGE模型需要大规模数据，常受隐私限制。本研究评估了联邦学习（FL）在缺血性中风患者（接受机械取栓治疗）中脑龄估计中的表现，并探讨其与临床表型及预后的关联。基于来自16家医院的1674例患者的FLAIR脑部影像，比较了三种数据管理策略：集中式学习（数据合并）、联邦学习（各中心本地训练）和单中心学习。报告了预测误差，并分析了脑龄与血管危险因素（如糖尿病、高血压、吸烟）以及卒中后3个月功能结局的关联。采用逻辑回归评估脑龄对结局的预测价值，校正了年龄、性别、血管危险因素、卒中严重程度、磁共振成像与动脉穿刺时间间隔、既往静脉溶栓及再通结果。结果显示，尽管集中式学习预测最准确，但联邦学习始终优于单中心模型。所有模型中，糖尿病患者脑龄均显著升高。良好与不良功能结局患者的比较及多变量预测分析均表明，脑龄与卒中后恢复具有显著关联。结论：联邦学习可在不集中数据的前提下实现准确脑龄预测。脑龄与血管危险因素及卒中后恢复的强关联，凸显其在卒中管理中的预后建模潜力。
原文摘要 · Abstract (English)
$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainAGE models requires large datasets, often restricted by privacy concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes. $\textbf{Methods:}$ We used FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome. $\textbf{Results:}$ While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery. $\textbf{Conclusion:}$ FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for prognostic modeling in stroke care.
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