Predicting amyloid risk by machine learning algorithms based on the A4 screen data: Application to the Japanese Trial‐Ready Cohort study

Abstract

Abstract Background Selecting cognitively normal elderly individuals with higher risk of brain amyloid deposition is critical to the success of prevention trials for Alzheimer's disease (AD). Methods Based on the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's Disease study data, we built machine‐learning models and applied them to our ongoing Japanese Trial‐Ready Cohort (J‐TRC) webstudy participants registered within the first 9 months (n = 3081) of launch to predict standard uptake value ratio (SUVr) of amyloid positron emission tomography. Results Age, family history, online Cognitive Function Instrument and CogState scores were important predictors. In a subgroup of J‐TRC webstudy participants with known amyloid status (n = 37), the predicted SUVr corresponded well with the self‐reported amyloid test results (area under the curve = 0.806 [0.619–0.992]). Discussion Our algorithms may be usable for automatic prioritization of candidate participants with higher amyloid risks to be preferentially recruited from the J‐TRC webstudy to in‐person study, maximizing efficiency for the identification of preclinical AD participants.

Keywords

• J‐TRC
• machine learning
• online recruitment
• preclinical Alzheimer's disease
• Trial‐Ready Cohort
• webstudy