The Neurological Field Continues to Update

Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which there is currently no cure, and its incidence is on the rise. Early detection is essential for timely intervention and slowing the progression of the disease. While the brain structures of healthy aging individuals change gradually, the aging trajectories in AD patients deviate significantly. Recent advancements in deep learning have enabled the detection of subtle changes in brain structures using neuroimaging data. By developing brain age prediction models based on data from healthy individuals, it is possible to estimate brain age at various stages of AD progression and assess the Brain Age Gap (BAG)—the difference between predicted brain age and chronological age—which holds promise as an early diagnostic biomarker for AD.