Yang Mengya, Elazab Ahmed, Yang Peng, Xia Zaimin, Wang Tianfu, Lei Baiying
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:281-284. doi: 10.1109/EMBC.2019.8857827.
Alzheimer's disease (AD), the most common type of the dementia, is a progressive neurodegenerative disease that mainly affects elderly. It causes a high financial burden for patients and their families. For effective treatment of AD, it is important to identify the AD progression of clinical disease over time. As the cognitive scores can effectively indicate the disease status, the prediction of the scores using the longitudinal magnetic resonance imaging (MRI) data is highly desirable. In this paper, we propose a joint learning and clinical scores prediction method for AD diagnosis via longitudinal MRI data. Specifically, we devise a novel feature selection method that consists of a temporally constrained group LASSO model and the correntropy. The baseline MRI data is used to jointly select the most discriminative features. Then, we use the stacked long short-term memory (SLSTM) to effectively capture useful information in the input sequence to predict the clinical scores of future time points. Extensive experiments on the Alzheimer's disease Neuroimaging Initiative (ADNI) database are conducted to demonstrate the effectiveness of the proposed model. Our model can accurately describe the relationship between MRI data and scores, and thus it can be effective in predicting longitudinal scores.
阿尔茨海默病（AD）是最常见的痴呆类型，是一种主要影响老年人的进行性神经退行性疾病。它给患者及其家庭带来了沉重的经济负担。为了有效治疗AD，确定临床疾病随时间的AD进展情况很重要。由于认知分数可以有效指示疾病状态，因此使用纵向磁共振成像（MRI）数据预测分数非常有必要。在本文中，我们提出了一种通过纵向MRI数据进行AD诊断的联合学习和临床分数预测方法。具体而言，我们设计了一种新颖的特征选择方法，该方法由时间约束组套索模型和核熵组成。利用基线MRI数据联合选择最具判别力的特征。然后，我们使用堆叠长短期记忆（SLSTM）有效地捕获输入序列中的有用信息，以预测未来时间点的临床分数。在阿尔茨海默病神经影像倡议（ADNI）数据库上进行了广泛的实验，以证明所提出模型的有效性。我们的模型可以准确描述MRI数据与分数之间的关系，因此能够有效地预测纵向分数。
