Presentation by Y.-W. Chang at SPIE-ETAI, San Diego, 24 August 2022

Deep-learning-detected tau deposition (color in orange) for Alzheimer’s Disease. (Image by Y.-W. Chang.)
Deep-learning analysis in tau PET for Alzheimer’s continuum
Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Submitted to SPIE-ETAI
Date: 24 August 2022
Time: 16:40 (PDT)

Previous studies have suggested that Alzheimer’s disease (AD) is typically characterized by abnormal accumulation of tau proteins in neurofibrillary tangles. This is usually assessed by measuring tau levels in regions of interest (ROIs) defined based on previous post-mortem studies. However, it remains unclear where this approach is suitable for assessing tau accumulation in vivo across the different stages of individuals. This study employed a data-driven deep learning approach to detect tau deposition across different AD stages at the voxel level. Moreover, the classification performance of this approach on distinguishing different AD stages was compared with the one using conventional ROIs.

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