Advancements and challenges in using AI for biomarker detection in early Alzheimer’s disease

Abstract

The rapid growth in Alzheimer’s disease (AD) research has led to an unprecedented accumulation of biomedical and clinical data, including longitudinal patient datasets and comprehensive observational cohort databases comprising clinical, biomedical, neuroimaging and lifestyle data. Expert use of machine learning algorithms is indispensable in order to realize the full potential of the data for diagnosis and drug target discovery. Here, we provide an overview of the biomedical and neuroimaging measures for AD diagnosis and staging. We then critically review the application of machine learning (classification) methods to AD data and provide insight for future improvements and research directions. Future research should aim to improve interpretability, accessibility and thorough validation of the models, enabling translation into clinical applications.