Cheah Wen-Ting, Hwang Jwu-Jia, Hong Sheng-Yi, Fu Li-Chen, Chang Yu-Ling, Chen Ta-Fu, Chen I-An, Chou Chun-Chen
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Department of Psychology, National Taiwan University, Taipei, Taiwan.
JMIR Med Inform. 2022 Mar 9;10(3):e31106. doi: 10.2196/31106.
Alzheimer disease (AD) and other types of dementia are now considered one of the world's most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia. In addition to reducing the physical and psychological stress of patients' caregivers in the long term, it will also improve the everyday quality of life of patients.
The aim of this study was to design a digital screening system to discriminate between patients with MCI and AD and healthy controls (HCs), based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological test.
The study took place at National Taiwan University between 2018 and 2019. In order to develop the system, pretraining was performed using, and features were extracted from, an open sketch data set using a data-driven deep learning approach through a convolutional neural network. Later, the learned features were transferred to our collected data set to further train the classifier. The first data set was collected using pen and paper for the traditional method. The second data set used a tablet and smart pen for data collection. The system's performance was then evaluated using the data sets.
The performance of the designed system when using the data set that was collected using the traditional pen and paper method resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.913 (SD 0.004) when distinguishing between patients with MCI and HCs. On the other hand, when discriminating between patients with AD and HCs, the mean AUROC was 0.950 (SD 0.003) when using the data set that was collected using the digitalized method.
The automatic ROCF test scoring system that we designed showed satisfying results for differentiating between patients with AD and MCI and HCs. Comparatively, our proposed network architecture provided better performance than our previous work, which did not include data augmentation and dropout techniques. In addition, it also performed better than other existing network architectures, such as AlexNet and Sketch-a-Net, with transfer learning techniques. The proposed system can be incorporated with other tests to assist clinicians in the early diagnosis of AD and to reduce the physical and mental burden on patients' family and friends.
阿尔茨海默病（AD）和其他类型的痴呆症现在被认为是全球老年人面临的最紧迫的健康问题之一。它是2019年全球第七大死因。随着痴呆症患者数量的增加以及治疗和护理成本的上升，在轻度认知障碍（MCI）阶段早期发现该疾病将防止痴呆症的快速进展。这不仅能长期减轻患者护理人员的身心压力，还能提高患者的日常生活质量。
本研究的目的是基于雷-奥斯特里茨复杂图形（ROCF）神经心理学测试设计一种数字筛查系统，以区分MCI患者、AD患者和健康对照（HCs）。
该研究于2018年至2019年在国立台湾大学进行。为了开发该系统，使用数据驱动的深度学习方法通过卷积神经网络对一个开放草图数据集进行预训练并提取特征。随后，将学习到的特征转移到我们收集的数据集上以进一步训练分类器。第一个数据集使用纸笔传统方法收集。第二个数据集使用平板电脑和智能笔进行数据收集。然后使用这些数据集评估系统的性能。
当使用传统纸笔方法收集的数据集时，设计的系统在区分MCI患者和HCs时，受试者操作特征曲线（AUROC）下的平均面积为0.913（标准差0.004）。另一方面，当区分AD患者和HCs时，使用数字化方法收集的数据集时，平均AUROC为0.950（标准差0.003）。
我们设计的自动ROCF测试评分系统在区分AD患者、MCI患者和HCs方面显示出令人满意的结果。相比之下，我们提出的网络架构比我们之前的工作表现更好，之前的工作未包括数据增强和随机失活技术。此外，它在使用迁移学习技术时也比其他现有网络架构（如AlexNet和Sketch-a-Net）表现更好。所提出的系统可以与其他测试相结合，以协助临床医生早期诊断AD，并减轻患者家人和朋友的身心负担。
