Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Brain aging is a complex, multifaceted process that can be challenging to model in ways that
are accurate and clinically useful. One of the most common approaches has been to apply …

The current study focuses on systematically analyzing the recent advances in the field of
Multimodal eXplainable Artificial Intelligence (MXAI). In particular, the relevant primary …

In computational neuroscience, there has been an increased interest in developing machine
learning algorithms that leverage brain imaging data to provide estimates of" brain age" for …

Most societies witness an ever increasing prevalence of both obesity and dementia, a
scenario related to often underestimated individual and public health burden. Overnutrition …

The recent advancements in the field of Artificial Intelligence (AI) translated to an increased
adoption of AI technology in the humanities, which is often challenged by the limited amount …

Convolutional Neural Networks (CNNs) are frequently and successfully used in medical
prediction tasks. They are often used in combination with transfer learning, leading to …

This study aimed to develop and validate machine learning (ML) models that predict age
using intracranial vessels' tortuosity and diameter features derived from magnetic resonance …

The lack of explainability of deep learning models limits the adoption of such models in
clinical practice. Prototype-based models can provide inherent explainable predictions, but …

Brain age estimation involves predicting an individual's biological age from their brain
images. This process offers valuable insights into the aging process and the progression of …