Introduction The difference between the chronological and biological brain age, called the
brain age gap (BAG), has been identified as a promising biomarker to detect deviation from …

Objective Distributed learning avoids problems associated with central data collection by
training models locally at each site. This can be achieved by federated learning (FL) …

Synthetic data have emerged as an attractive option for develo** machine-learning
methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a …

Recent years have seen a growing interest in methods for predicting an unknown variable of
interest, such as a subject's diagnosis, from medical images depicting its anatomical …

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes,
which if harnessed appropriately, can contribute to advancements in various sectors, from …

Acute ischemic stroke is a leading cause of mortality and morbidity worldwide. Timely
identification of the extent of a stroke is crucial for effective treatment, whereas spatio …

Brain aging is an inevitable process in adulthood, yet there is a lack of objective measures to
accurately assess its extent. This study aims to develop brain age prediction model using …

Introduction The rate of neurodegeneration in multiple sclerosis (MS) is an important
biomarker for disease progression but can be challenging to quantify. The brain age gap …

Sharing multicenter imaging datasets can be advantageous to increase data diversity and
size but may lead to spurious correlations between site-related biological and non-biological …

Background Supervised machine learning models have been widely used to predict and get
insight into diseases by classifying patients based on personal health records. However, a …