Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.

The brain-predicted age difference (brain-PAD) is associated with measures of clinical interest in people with multiple sclerosis (pwMS). Most brain age models rely on 3D T1-weighted scans, which are not routinely acquired in MS clinical practice, limiting their potential for clinical translation. We aimed to develop a model predicting brain age using T2-FLAIR, the core sequence for MS diagnosis and monitoring, and validate the resulting brain-PAD values as a biomarker of MS severity and progression. We collected 3D T2-FLAIR and 3D T1-weighted brain MRI scans to compose (i) a multicentre cohort of healthy participants for brain age modeling, and (ii) a single-centre cohort of pwMS and healthy controls for external validation. We trained and evaluated 3D convolutional neural network models predicting brain age from T2-FLAIR or T1-weighted images. Models were compared using t-tests based on bootstrapped standard errors. Saliency maps were obtained with the SmoothGrad method to visualize regions that were most important for the predictions. Finally, using a linear model framework, we clinically validated the resulting brain-PAD metric by assessing its relationship with diagnosis (MS versus healthy controls), clinical phenotype, disease duration, and physical disability as measured with the Expanded Disability Status Scale (EDSS), adjusting for age and sex. The Inception-ResNet-V2 model based on T2-FLAIR scans yielded accurate brain age predictions (test set MAE = 3.31 years, R