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Background: Amyloid-ß positivity (Aß+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer’s disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aß positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. Objective: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aß+ based on gold-standard of PET imaging.

Methods: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aß status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers.

Results: The models that included NP and MRI …