Abstract
$\textbf{Objective:}$ 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.
Abstract (translated)
**目标:** 大脑预测年龄差（BrainAGE）是一种反映大脑健康的神经影像生物标志物。然而，训练稳健的BrainAGE模型需要大量数据集，这通常受到隐私问题的限制。本研究评估了在缺血性卒中患者接受机械取栓治疗的情况下，联邦学习（FL）用于BrainAGE估计的效果，并探讨其与临床表型和功能结局的关系。 **方法:** 我们使用来自16家医院中心共1674名卒中患者的FLAIR脑部图像。我们实施了标准机器学习和深度学习模型，在三种数据管理策略下进行BrainAGE估算：集中式学习（合并所有数据）、联邦学习（在每个站点本地训练）以及单个站点学习。报告了预测误差，并考察了BrainAGE与血管风险因素（如糖尿病、高血压、吸烟）及卒中后3个月的功能结局之间的关系。通过逻辑回归评估调整年龄、性别、血管风险因素、卒中严重程度、MRI和动脉穿刺之间的时间间隔、先前的静脉溶栓以及再通结局后的脑龄对这些结果的预测价值。 **结果:** 虽然集中式学习提供了最准确的预测，但联邦学习在所有情况下均优于单一站点模型。对于所有模型而言，糖尿病患者的BrainAGE显著较高。将具有良好和较差功能结局的患者进行比较，并通过多元回归预测这些结局显示了BrainAGE与卒中后恢复之间的关联的重要性。 **结论:** 联邦学习可以在不集中化数据的情况下实现准确的年龄预测。脑龄与血管风险因素及卒中后的恢复之间存在显著的相关性，这突显了其在卒中护理预后模型中的潜在价值。
URL
https://arxiv.org/abs/2506.15626
