2018/04/05 by Enrico Pellegrini, Lucia Ballerini, Pellegrini, Enrico +28
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Dementia and Cognitive Impairment Research #FOS: Biological sciences #FOS: Computer and information sciences #Neurons and Cognition (q-bio.NC) #cs.CV #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1804.01961
openalex publication_date 2018/04/05 · arxiv created 2018/04/11 · arxiv updated 2018/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
INTRODUCTION: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy ageing through to dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs healthy controls, used ADNI data, support vector machines and only T1-weighted sequences. Accuracy was highest for differentiating AD from healthy controls, and poor for differentiating healthy controls vs MCI vs AD, or MCI converters vs non-converters. Accuracy increased using combined data types, but not by data source, sample size or machine learning method. DISCUSSION: Machine learning does not differentiate clinically-relevant disease categories yet. More diverse datasets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.