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FastSurfer-Based Brain Morphometry and Machine-Learning Classification Across the Alzheimer’s Disease Spectrum

Background: This study aimed to quantitatively assess structural changes in the hippocampus, amygdala, entorhinal cortex, lateral ventricles, precuneus, and posterior cingulate using deep-learning-based FastSurfer morphometry and evaluate their contribution to classification across the Alzheimer’s disease (AD) spectrum...