Paper Info
Title
Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis.
Abstract
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 Alzheimeru0027s 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.
Year
Venue
Field
2018
arXiv: Neurons and Cognition
Data source,Disease,Support vector machine,Artificial intelligence,Neuroimaging,Mathematics,Sample size determination,Machine learning,Cognitive impairment,Dementia
DocType
Volume
Citations 
Journal
abs/1804.01961
0
PageRank 
References 
Authors
0.34
65
14
Name
Order
Citations
PageRank
Enrico Pellegrini151.50
Lucia Ballerini215617.70
Maria C Valdés Hernández33210.14
Francesca M. Chappell400.34
Víctor González-Castro59010.70
Devasuda Anblagan6111.91
Samuel Danso751.44
Susana Muñoz Maniega800.34
Dominic Job925518.41
Cyril R. Pernet1012212.32
Grant Mair1151.08
T MacGillivray12234.29
Emanuele Trucco131236116.32
Joanna M. Wardlaw1410817.52