Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning

Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this paper, we use deep learning with brain network and...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 16; číslo 1; s. 244 - 257
Hlavní autoři: Ju, Ronghui, Hu, Chenhui, Zhou, Pan, Li, Quanzheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Aged
Aged, 80 and over
Aging
Alzheimer Disease - diagnostic imaging
Alzheimer's disease
Brain
Brain - diagnostic imaging
Brain mapping
Brain Mapping - methods
Brain network
Classifiers
Cognitive ability
Correlation
Deep Learning
Dementia
Diagnosis
Early Diagnosis
Feature extraction
Female
Functional magnetic resonance imaging
Health care
Humans
Image Interpretation, Computer-Assisted - methods
Learning algorithms
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Middle Aged
Multimedia
Neural networks
Neurodegenerative diseases
Neuroimaging
Prediction models
ISSN:1545-5963, 1557-9964, 1557-9964
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Shrnutí:Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this paper, we use deep learning with brain network and clinical relevant text information to make early diagnosis of Alzheimer's Disease (AD). The clinical relevant text information includes age, gender, and <inline-formula><tex-math notation="LaTeX">ApoE</tex-math> <inline-graphic xlink:href="ju-ieq1-2776910.gif"/> </inline-formula> gene of the subject. The brain network is constructed by computing the functional connectivity of brain regions using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection. Compared to traditional classifiers based on R-fMRI time series data, about 31.21 percent improvement of the prediction accuracy is achieved by the proposed deep learning method, and the standard deviation reduces by 51.23 percent in the best case that means our prediction model is more stable and reliable compared to the traditional methods. Our work excavates deep learning's advantages of classifying high-dimensional multimedia data in medical services, and could help predict and prevent AD at an early stage.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2017.2776910