Department of Radiology, Zibo Central Hospital, Zibo 255000, Shandong, China.
China-Israel fMRI Precision Neuroimaging Joint Laboratory, Zibo 255000, Shandong, China.
J Healthc Eng. 2021 Oct 19;2021:8198552. doi: 10.1155/2021/8198552. eCollection 2021.
The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.
本研究旨在探索深度度量学习（DML）算法在阿尔茨海默病（AD）患者磁共振成像（MRI）检查中的准确性和稳定性。本研究中，患者的 MRI 数据来自阿尔茨海默病神经影像学倡议（ADNI）数据库（共 180 例 AD 病例，88 名女性，92 名男性；188 例健康状况（HC）样本，包括 90 名女性和 98 名男性。210 例轻度认知障碍（MCI）样本，104 名女性和 106 名男性）。在深度学习的基础上，使用卷积神经网络（CNN）和 DML 算法构建早期 AD 诊断系统。然后，该系统用于对 AD、HC 和 MCI 进行分类，并比较两种算法在 MRI 图像分类中的准确性和稳定性。结果发现，在 AD 和 HC 的分类中，深度度量学习模型的分类准确率和灵敏度均为 0.83，优于 CNN 模型；特异性方面，DML 模型的分类特异性为 0.82，略低于 CNN 模型；在 MCI 和 HC 的分类中，DML 模型的分类准确率和灵敏度为 0.65，优于 CNN 模型；特异性方面，DML 模型的分类特异性为 0.66，略低于 CNN 模型。这表明 DML 模型对早期 AD 患者的分类效果更好。损失曲线分析结果表明，对于 AD 和 HC 或 MCI 和 HC 的分类，DML 算法可以提高 AD 早期预测模型的收敛速度。因此，DML 算法可以显著提高 MRI 图像的清晰度和质量，提高早期 AD 患者的分类准确性和稳定性，加速模型的收敛，为 AD 的早期预测提供了新途径。
