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Diagnosis of Alzheimer’s Disease Using Brain Imaging: State of the Art

  • Atif ShahEmail author
  • Kamal Niaz
  • Moataz Ahmed
  • Reem Bunyan
Chapter

Abstract

Alzheimer’s disease (AD) is one of the prominent diseases in elderly people which leads to language impairment, disorientation, memory loss, and eventually death. Despite the severity of the disease, there is no such drug reported to control, reduce, or stop the progression of AD. The neuroimages played a crucial role in tracking the progression of AD using biomarkers which help the physicians to diagnose the disease more accurately. In this study, we investigate the effectiveness of structural and functional neuroimaging modularities which are used in the state-of-the-art methods to diagnose AD. The finding shows that most of the studies prioritize magnetic resonance imaging techniques (MRIT) solely or combined with other neuroimaging modularities to achieve better performance. Studies also founded that only few public datasets are available, and the most widely used public dataset is Alzheimer’s Disease Neuroimaging Initiative.

Keywords

Medical imaging MRI Alzheimer’s disease CAD Features extraction 

Abbreviations

3D

Three dimensions

ACC

Accuracy

AD

Alzheimer’s disease

ADNI

Alzheimer’s Disease Neuroimaging Initiative

AUC

Area under the curve

Amyloid-β

CAD

Computer-aided diagnosis

CNN

Convolutional neural network

CR

Creatine

CSF

Cerebrospinal fluid

CT

Computed tomography

DL

Deep learning

DTI

Diffusion tensor imaging

DWI

Diffusion-weighted imaging

FC

Functional connectivity

FDG

Fluorodeoxyglucose

fMRI

Functional magnetic resonance imaging

FN

False-negative

FP

False-positive

GP-LR

Gaussian process logistic regression

ICA

Independent component analysis

LLE

Local linear embedding

MCI

Mild cognitive impairment

MCI-A

Amnestic MCI

MCI-C

MCI converted

MCI-NC

MCI non-converted

MCI-P

Progressive MCI

MCI-S

Stable MCI

MRI

Magnetic resonance imaging

MRS

Magnetic resonance spectroscopy

NAA

N-Acetyl aspartate

PCA

Principal component analysis

PET

Positron emission tomography

RBF

Radial basis function

re-fMRI

Resting-state fMRI

RFE

Recursive feature elimination

ROC

Receiver operating characteristic

ROS

Reactive oxygen species

SEN

Sensitivity

SPE

Specificity

SPECT

Single photon emission computer tomography

SVM

Support vector machine

T1-w

T1-weighted

T2-w

T2-weighted

TN

True-negative

TP

True-positive

VBM

Voxel-based measure

WM

White matter

WMH

White matter hyperintensities

Notes

Acknowledgments

The authors wish to acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for utilizing the various facilities in carrying out this research. Other authors of the manuscript thank and acknowledge their respective universities and institutes as well.

Conflict of Interest

There is no conflict of interest.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information and Computer ScienceKing Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Department of Pharmacology and Toxicology, Faculty of Bio-SciencesCholistan University of Veterinary and Animal Sciences (CUVAS)BahawalpurPakistan
  3. 3.Neurosciences CenterKing Fahd Specialist Hospital DammamDammamKingdom of Saudi Arabia

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