Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

Abstract

Alzheimer’s disease (AD) is a type of brain disease that affects a person’s ability to perform daily tasks. Modern neuroimaging techniques have made it possible to detect structural and functional changes in the brain that are linked to AD, and machine learning (ML)-based methods have been extensively developed to help physicians achieve fast and accurate imaging-based AD detection. One critical issue when deploying ML methods in clinical applications is the domain shift that exists between the training and test data, which may significantly attenuate a model’s performance. To resolve this issue, domain adaptation (DA) is needed to narrow the performance gap between data from domains with different distributions. The purpose of this review is to offer insight into the state of ML and DA research in the field of neuroimaging-based AD detection. The limitations of existing studies, as well as opportunities for future studies, are discussed with the hope that more investigations will be conducted in the future to optimize the clinical workflow for AD diagnosis and treatment.