Artificial Intelligence for Automatic Detection of Alzheimer's Disease: Resolving Domain Shift Problems to Create Reliable Models

Alzheimer's disease (AD) is a brain disorder that impairs one's ability to function on a daily basis. Machine learning (ML) has significantly helped clinicians in quickly and accurately identifying AD through imaging-based methods. Advanced neuroimaging techniques have made it possible to detect structural and functional abnormalities in the brain associated with AD. However, applying ML to clinical settings poses challenges due to differences in data distribution between training and test datasets, which can significantly impact model performance. To address this challenge, the domain adaptation (DA) approach is necessary, as data from different distribution domains present a performance gap. This review focuses on current research involving ML and DA techniques for early AD detection using neuroimaging data, and aims to encourage further research efforts to improve the clinical workflow for diagnosing and treating AD. The review also includes a discussion of the limitations of current studies and possible research directions.