Song Jaeyoon, Cho Eunseo, Lee Huiseop, Lee Suyoung, Kim Sehyeon, Kim Jinsik
Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea.
Biosensors (Basel). 2025 Feb 11;15(2):102. doi: 10.3390/bios15020102.
Monitoring and assessing the progression of symptoms in neurodegenerative diseases, including Alzheimer's and Parkinson's disease, are critical for improving patient outcomes. Traditional biomarkers, such as cerebrospinal fluid analysis and brain imaging, are widely used to investigate the underlying mechanisms of disease and enable early diagnosis. In contrast, digital biomarkers derived from phenotypic changes-such as EEG, eye movement, gait, and speech analysis-offer a noninvasive and accessible alternative. Leveraging portable and widely available devices, such as smartphones and wearable sensors, digital biomarkers are emerging as a promising tool for ND diagnosis and monitoring. This review highlights the comprehensive developments in digital biomarkers, emphasizing their unique advantages and integration potential alongside traditional biomarkers.
监测和评估神经退行性疾病(包括阿尔茨海默病和帕金森病)的症状进展对于改善患者预后至关重要。传统生物标志物,如脑脊液分析和脑成像,被广泛用于研究疾病的潜在机制并实现早期诊断。相比之下,从表型变化(如脑电图、眼动、步态和语音分析)中衍生出的数字生物标志物提供了一种非侵入性且易于获取的替代方法。利用智能手机和可穿戴传感器等便携式且广泛可用的设备,数字生物标志物正成为神经退行性疾病诊断和监测的一种有前景的工具。本综述重点介绍了数字生物标志物的全面发展,强调了它们相对于传统生物标志物的独特优势和整合潜力。