作者
Enrico Pellegrini, Lucia Ballerini, Maria del C Valdes Hernandez, Francesca M Chappell, Victor González-Castro, Devasuda Anblagan, Samuel Danso, Susana Muñoz-Maniega, Dominic Job, Cyril Pernet, Grant Mair, Tom J MacGillivray, Emanuele Trucco, Joanna M Wardlaw
发表日期
2018/1/1
来源
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
卷号
10
页码范围
519-535
出版商
No longer published by Elsevier
简介
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 aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.
Results
Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine …
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E Pellegrini, L Ballerini, MCV Hernandez, FM Chappell… - Alzheimer's & Dementia: Diagnosis, Assessment & …, 2018