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End-of-life studies have validated the binary visual reads of 18 F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults.

Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth