Alzheimer’s disease digital biomarkers multidimensional landscape and AI model scoping review
Wenhao Qi, Xiaohong Zhu, Bin Wang, Yankai Shi, Chaoqun Dong, Shiying Shen, Jiaqi Li, Kun Zhang, Yunfan He, Mengjiao Zhao, Shiyan Yao, Yongze Dong, Huajuan Shen, Junling Kang, Xiaodong Lü, Guowei Jiang, Lizzy Boots, Heming Fu, Pan Li, Hongkai Chen, Zhenyu Yan, Guoliang Xing, Shihua Cao
Abstract
As digital biomarkers gain traction in Alzheimer's disease (AD) diagnosis, understanding recent advancements is crucial. This review conducts a bibliometric analysis of 431 studies from five online databases: Web of Science, PubMed, Embase, IEEE Xplore, and CINAHL, and provides a scoping review of 86 artificial intelligence (AI) models. Research in this field is supported by 224 grants across 54 disciplines and 1403 institutions in 44 countries, with 2571 contributing researchers. Key focuses include motor activity, neurocognitive tests, eye tracking, and speech analysis. Classical machine learning models dominate AI research, though many lack performance reporting. Of 21 AD-focused models, the average AUC is 0.887, while 45 models for mild cognitive impairment show an average AUC of 0.821. Notably, only 2 studies incorporated external validation, and 3 studies performed model calibration. This review highlights the progress and challenges of integrating digital biomarkers into clinical practice.