Almubark Ibrahim, Chang Lin-Ching, Shattuck Kyle F, Nguyen Thanh, Turner Raymond Scott, Jiang Xiong
Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States.
Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States.
Front Aging Neurosci. 2020 Dec 3;12:603179. doi: 10.3389/fnagi.2020.603179. eCollection 2020.
The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both. A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used. Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%. Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.
本研究的目的是调查和比较机器学习利用标准神经心理学测试的行为数据、一项认知任务的数据或两者的数据进行分类的性能。对8名轻度认知障碍（MCI）患者、8名轻度阿尔茨海默病（AD）患者和41名人口统计学匹配的对照者（CN）进行了一套神经心理学测试和一项简单的5分钟认知任务。使用了一个全连接多层感知器（MLP）网络和四种有监督的传统机器学习算法。传统机器学习算法利用神经心理学或认知数据取得了相似的分类性能。MLP在利用认知数据（单独或与神经心理学数据一起）时的表现优于传统算法，但在利用神经心理学数据时并非如此。特别是，结合神经心理学测试和认知任务的汇总分数的MLP实现了约90%的灵敏度和约90%的特异性。将这些模型应用于一个独立数据集（其中参与者在人口统计学上与主要数据集中的参与者不同）时，保持了较高的特异性（100%），但灵敏度降至66.67%。利用特定认知任务的数据进行深度学习有望辅助阿尔茨海默病的早期诊断，但未来需要开展涉及大量多样样本的研究来验证和改进这种方法。
