Li Shuwu, Li Kai, Liu Jiakang, Huang Shouqiang, Wang Chen, Tu Yuting, Wang Bo, Zhang Pengpeng, Luo Yuntian, Zhang Yanli, Chen Tong
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
School of Information Engineering, Hangzhou Medical College, Hangzhou, China.
Front Comput Neurosci. 2025 Jun 4;19:1564932. doi: 10.3389/fncom.2025.1564932. eCollection 2025.
In response to the shortcomings of the current Alzheimer's disease (AD) early populations assessment, which is based on neuropsychological scales with high subjectivity, low accuracy of repeated measurements, tedious process and dependence on physicians, it was found that digital biomarkers based on the writing process can effectively characterize the cognitive deficits of patients with mild cognitive impairment (MCI) due to AD.
This study designed a digital writing assessment paradigm, extracted dynamic handwriting and image data during the paradigm assessment process, and analyzed digital biomarkers of the writing process to assess subjects' cognitive functions. A total of 72 subjects, including 34 health controls (HC) and 38 MCI due to AD, were enrolled in this study.
Their combined screening efficacy of digital biomarkers based on the MCI writing process due to AD populations having an area under curve (AUC) of 0.918, and a confidence interval (CI) of 0.854-0.982, was higher than the Montreal Cognitive Assessment Scale (AUC = 0.859, CI = 0.772-0.947) and the Mini-mental State Examination Scale (AUC = 0.783, CI = 0.678-0.888).
Therefore, digital biomarkers based on the writing process can characterize and quantify the cognitive function of MCI due to AD populations at a fine-grained level, which is expected to be a new method for intelligent screening and early warning of early AD populations in a community-based physician-free setting.
针对当前阿尔茨海默病（AD）早期人群评估基于神经心理量表主观性高、重复测量准确性低、过程繁琐且依赖医生的缺点，发现基于书写过程的数字生物标志物能够有效表征因AD导致的轻度认知障碍（MCI）患者的认知缺陷。
本研究设计了一种数字书写评估范式，在范式评估过程中提取动态笔迹和图像数据，并分析书写过程的数字生物标志物以评估受试者的认知功能。本研究共纳入72名受试者，包括34名健康对照（HC）和38名因AD导致的MCI患者。
基于因AD人群的MCI书写过程的数字生物标志物的联合筛查效能，其曲线下面积（AUC）为0.918，置信区间（CI）为0.854 - 0.982，高于蒙特利尔认知评估量表（AUC = 0.859，CI = 0.772 - 0.947）和简易精神状态检查表（AUC = 0.783，CI = 0.678 - 0.888）。
因此，基于书写过程的数字生物标志物能够在细粒度水平表征和量化因AD人群的MCI认知功能，有望成为在基于社区的无医生环境中对AD早期人群进行智能筛查和早期预警的新方法。
