Classification of amyloid positivity in PET imaging using end‐to‐end deep learning: a multi‐cohort, multi‐tracer analysis

Positron emission tomography (PET) imaging with amyloid‐specific tracers is the gold standard for assessing amyloid positivity in vivo. However, visual interpretation of PET images is limited by availability of clinicians and interobserver variability. Classification of amyloid status typically requires calculation of standardized uptake value ratios (SUVRs), Correspondence with neuroanatomy from magnetic resonance imaging, and additional complex, computationally intensive preprocessing steps requiring specialized software. We developed a deep learning model that can automatically classify unprocessed PET images as amyloid positive or negative.