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.

中文翻译：

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用于检测阿尔茨海默病过程的基于 MRI 的自动深度学习模型

在神经病理性疾病的背景下，神经影像学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。本研究应用了先进的深度学习方法，一种新型的脑成像技术，以评估其对提高 AD 诊断准确性的贡献。三维卷积神经网络 (3D-CNN) 与磁共振成像 (MRI) 一起应用来执行二元和三元疾病分类模型。来自阿尔茨海默病神经影像学计划 (ADNI) 的数据集用于比较 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-SVM 三元分类的准确率结果为 [公式：见正文]%，[公式：见正文]%和[公式：见正文]%。3D-CNN-SVM 对 NC、MCI 和 AD 诊断的三元分类准确率分别为 93.71%、96.82% 和 96.73%。此外，3D-CNN-SVM 在二元分类方面表现出最佳性能。我们的研究表明，“NC 与 MCI”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%；和“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 与 MRI 相比产生了更好的性能。此外，3D-CNN-SVM 被证明是高效的，无需手动执行任何先前的特征提取，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 模式的安全性、无创性和无辐射特性，3D-CNN-SMV 可作为普通人群中 AD 的有效筛查选择。这项研究在区分 AD 和 MCI 受试者与正常对照组以及改善临床实践中基于价值的患者护理方面具有价值。由于 MRI 模式的非侵入性和非辐射特性，3D-CNN-SMV 可作为一般人群中 AD 的有效筛查选择。这项研究在区分 AD 和 MCI 受试者与正常对照组以及改善临床实践中基于价值的患者护理方面具有价值。由于 MRI 模式的非侵入性和非辐射特性，3D-CNN-SMV 可作为一般人群中 AD 的有效筛查选择。这项研究在区分 AD 和 MCI 受试者与正常对照组以及改善临床实践中基于价值的患者护理方面具有价值。