Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.
Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.
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DOI：
10.1038/s41598-018-22871-z
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发表时间：
2018-04-09
影响因子：
4.6
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Lu D;Popuri K;Ding GW;Balachandar R;Beg MF;Alzheimer’s Disease Neuroimaging Initiative
阿尔茨海默病（AD）是一种进行性神经退行性疾病，基于病理生理学的疾病生物标志物可能能够为疾病诊断和分期提供客观措施。通过MRI获得的神经成像扫描和FDG-PET获得的代谢图像提供了活体大脑结构和功能（葡萄糖代谢）的体内测量。假设结合多种不同的图像模式提供互补的信息可以帮助提高AD的早期诊断。在本文中，我们提出了一种新的基于深度学习的框架，利用多模态和多尺度深度神经网络来区分AD个体。我们的方法在识别轻度认知障碍（MCI）患者在转换前3年转化为AD的准确率为82.4%(1-3年内转换的综合准确率为86.4%)，对临床诊断可能为AD的个体进行分类的灵敏度为94.23%，对非痴呆对照组进行分类的特异性为86.3%。
Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.
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影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
5.7
作者：
Cho Y;Seong JK;Jeong Y;Shin SY;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
DOI：
10.1109/tbme.2015.2404809
发表时间：
2015-07
期刊：
IEEE transactions on bio-medical engineering
影响因子：
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作者：
Cheng B;Liu M;Zhang D;Munsell BC;Shen D
通讯作者：
Shen D
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L