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

ABSTRACT:

Purpose

End-of-life studies have validated the binary visual reads of ¹⁸F-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.

Methods

We applied SVM with a linear kernel to an ¹⁸F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10%

SUBMITTER: Reinartz M 

PROVIDER: S-EPMC9399207 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature