Imperial College London
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DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Reader
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ahmed:2018:10.1109/RBME.2018.2886237,
author = {Ahmed, MR and Zhang, Y and Feng, Z and Lo, B and Inan, OT and Liao, H},
doi = {10.1109/RBME.2018.2886237},
journal = {IEEE Reviews in Biomedical Engineering},
pages = {19--33},
title = {Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects},
url = {http://dx.doi.org/10.1109/RBME.2018.2886237},
volume = {12},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Ahmed,MR
AU  - Zhang,Y
AU  - Feng,Z
AU  - Lo,B
AU  - Inan,OT
AU  - Liao,H
DO  - 10.1109/RBME.2018.2886237
EP  - 33
PY  - 2018///
SN  - 1941-1189
SP  - 19
TI  - Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects
T2  - IEEE Reviews in Biomedical Engineering
UR  - http://dx.doi.org/10.1109/RBME.2018.2886237
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30561351
UR  - http://hdl.handle.net/10044/1/75186
VL  - 12
ER  -
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