Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction
Journal:
arXiv
Published Date:
Jun 18, 2025
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.
