IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):244-257. doi: 10.1109/TCBB.2017.2776910. Epub 2017 Nov 23.
Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this paper, we use deep learning with brain network and clinical relevant text information to make early diagnosis of Alzheimer's Disease (AD). The clinical relevant text information includes age, gender, and ApoE gene of the subject. The brain network is constructed by computing the functional connectivity of brain regions using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection. Compared to traditional classifiers based on R-fMRI time series data, about 31.21 percent improvement of the prediction accuracy is achieved by the proposed deep learning method, and the standard deviation reduces by 51.23 percent in the best case that means our prediction model is more stable and reliable compared to the traditional methods. Our work excavates deep learning's advantages of classifying high-dimensional multimedia data in medical services, and could help predict and prevent AD at an early stage.
由于医学成像和机器学习技术的进步，计算机医疗得到了快速发展。特别是，深度学习的最新进展为基于多媒体的临床决策支持开辟了一个新时代。在本文中，我们使用基于脑网络和临床相关文本信息的深度学习来进行阿尔茨海默病（AD）的早期诊断。临床相关文本信息包括受试者的年龄、性别和 ApoE 基因。脑网络是通过使用静息态功能磁共振成像（R-fMRI）数据计算脑区的功能连接来构建的。建立了一个有针对性的自动编码器网络，以区分正常老化和轻度认知障碍，这是 AD 的早期阶段。所提出的方法有效地揭示了有区别的脑网络特征，并为 AD 检测提供了可靠的分类器。与基于 R-fMRI 时间序列数据的传统分类器相比，所提出的深度学习方法的预测精度提高了约 31.21%，在最佳情况下，标准偏差降低了 51.23%，这意味着与传统方法相比，我们的预测模型更稳定和可靠。我们的工作挖掘了深度学习在医疗服务中对高维多媒体数据进行分类的优势，并有助于早期预测和预防 AD。
