Zhou Xiao, Kedia Sanchita, Meng Ran, Gerstein Mark
Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, United States of America.
Department of Computer Science, Yale University, New Haven, CT, United States of America.
PLoS One. 2024 Dec 4;19(12):e0312848. doi: 10.1371/journal.pone.0312848. eCollection 2024.
The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic resonance imaging (fMRI) data. However, we found that the genetic factors do not notably enhance the AD prediction by imaging. Thus, we focus on building an effective imaging-only model. In particular, we utilize data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a 3D Convolutional Neural Network (CNN) to analyze fMRI scans. Despite the limitations posed by our dataset (small size and imbalanced nature), our CNN model demonstrates accuracy levels reaching 92.8% and an ROC of 0.95. Our research highlights the complexities inherent in integrating multimodal medical datasets. It also demonstrates the potential of deep learning in medical imaging for AD prediction.
阿尔茨海默病（AD）的早期检测被认为对有效干预和管理至关重要。在此，我们探索用于AD早期检测的深度学习方法。我们同时考虑遗传风险因素和功能磁共振成像（fMRI）数据。然而，我们发现遗传因素并未显著增强基于成像的AD预测。因此，我们专注于构建一个仅基于成像的有效模型。具体而言，我们利用来自阿尔茨海默病神经影像倡议（ADNI）的数据，采用三维卷积神经网络（CNN）来分析fMRI扫描。尽管我们的数据集存在局限性（规模小且性质不均衡），但我们的CNN模型准确率达到了92.8%，受试者工作特征曲线下面积（ROC）为0.95。我们的研究突出了整合多模态医学数据集所固有的复杂性。它还展示了深度学习在医学成像中用于AD预测的潜力。
