Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
Date Issued
2018-12-11
Date Acceptance
2018-12-02
Citation
IEEE Reviews in Biomedical Engineering, 2018, 12, pp.19-33
ISSN
1941-1189
Publisher
Institute of Electrical and Electronics Engineers
Start Page
19
End Page
33
Journal / Book Title
IEEE Reviews in Biomedical Engineering
Volume
12
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
British Council (UK)
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/30561351
Grant Number
540213 SeNTH plus
2017-RLWK9-11046
Subjects
Algorithms
Alzheimer Disease
Cognitive Dysfunction
Dementia
Early Diagnosis
Humans
Image Interpretation, Computer-Assisted
Machine Learning
Multimodal Imaging
Neuroimaging
Pattern Recognition, Automated
Humans
Dementia
Alzheimer Disease
Image Interpretation, Computer-Assisted
Early Diagnosis
Algorithms
Pattern Recognition, Automated
Neuroimaging
Multimodal Imaging
Machine Learning
Cognitive Dysfunction
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2018-12-11