Wang Yujia, Chen Tong, Wang Chen, Ogihara Atsushi, Ma Xiaowen, Huang Shouqiang, Zhou Siyu, Li Shuwu, Liu Jiakang, Li Kai
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Brain Sci. 2023 Jan 31;13(2):244. doi: 10.3390/brainsci13020244.
The early identification of mild cognitive impairment (MCI) due to Alzheimer's disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human-computer interaction (HCI) paradigm for MCI due to AD in community screening.
在阿尔茨海默病(AD)早期识别出由其导致的轻度认知障碍(MCI),能够扩大AD的预警窗口。我们提出了一种评估空间执行过程(SEP)的新能力指标,该指标可动态评估空间导航任务中的执行过程。我们提出假说,即正常认知(NC)老年人与患有MCI的AD患者之间存在神经行为差异,这在SEP期间捕获的数字生物标志物中有所体现。据此,我们设计了一种针对AD所致MCI的新型2分钟智能移动警报方法,用于社区筛查。通过逐步回归分析选择了两个数字生物标志物,即总任务执行距离(METRtotal)和横向障碍物上方的执行距离(EDabove)。对于受教育年限超过9年的参与者,这两种数字生物标志物联合用于AD所致MCI的警报效率可达0.83。该方法具有速度快、警报效率高、成本低和智能化程度高的优点,因此在发展中国家的社区筛查中具有很高的应用价值。它还为社区筛查中AD所致MCI提供了一种基于人机交互(HCI)范式的新型智能警报方法。