Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process
Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process
复制标题
基于 MRI 的自动化深度学习模型，用于检测阿尔茨海默病过程
DOI：
10.1142/s012906572050032x
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发表时间：
2020-06-01
影响因子：
8
通讯作者：
Guo, Xiuhua
中科院分区：
文献类型：
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作者：
Feng, Wei;Van Halm-Lutterodt, Nicholas;Guo, Xiuhua
在神经病理障碍的背景下，神经成像已被广泛接受为诊断阿尔茨海默病(AD)和轻度认知障碍(MCI)患者的临床工具。本研究应用先进的深度学习方法这一新的脑成像技术，评价其对提高AD诊断准确率的作用。将三维卷积神经网络(3D-CNN)与磁共振成像(MRI)相结合，执行二值和三值疾病分类模型。来自阿尔茨海默病神经成像计划(ADNI)的数据集被用于比较3D-CNN、3D-CNN-支持向量机(SVM)和二维(2D)-CNN模型的深度学习性能。2D-CNN、3D-CNN和3D-CNN-支持向量机三元分类的准确率分别为[公式：见文本]%、[公式：见文本]%和[公式：见文本]%。3D-CNN-支持向量机对NC、MCI和AD的三元分类准确率分别为93.71%、96.82%和96.73%。此外，3D-CNN-支持向量机表现出了最好的二值分类性能。结果显示，NC与AD的准确性、敏感性和特异性分别为98.90%、98.90%和98.80%，NC与AD的准确性、敏感性和特异性分别为99.10%、99.80%和98.40%，MCI与AD的准确性、敏感性和特异性分别为89.40%、86.70%和84.00%。这项研究清楚地表明，与目前使用的深度学习方法相比，3D-CNN-SVM在MRI方面具有更好的性能。此外，3D-CNN-支持向量机被证明是有效的，无需人工执行任何事先的特征提取，并且完全独立于成像协议和扫描仪的可变性。这表明它有可能被未经培训的操作员利用，并扩展到虚拟患者成像数据。此外，由于MRI的安全性、非侵入性和无辐射特性，3D-CNN-SMV可以作为普通人群中AD的有效筛查选择。本研究对区分AD和MCI患者与正常对照以及改善临床实践中对患者的价值护理具有一定的价值。
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that 'NC versus MCI' showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; 'NC versus AD' showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and 'MCI versus AD' showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.