Xu Ying, Zhang Chi, Pan Baobao, Yuan Qing, Zhang Xu
Shenzhen Bao'an Centre for Chronic Disease Control, Shenzhen, PR China.
Shenzhen Yiwei Technology, Shenzhen, PR China.
NPJ Digit Med. 2024 Aug 22;7(1):219. doi: 10.1038/s41746-024-01206-5.
Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer's Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA's potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.
痴呆症是一项重大的全球健康挑战，临床前阶段的早期筛查对于有效管理至关重要。阿尔茨海默病是最常见的痴呆症形式，其传统诊断生物标志物受到成本和侵入性的限制。轻度认知障碍（MCI）是痴呆症的前驱症状，目前通过蒙特利尔认知评估量表（MoCA）等神经心理学测试来识别，但这些测试不适用于大规模筛查。眼动追踪技术能够捕捉和量化与认知行为相关的眼动，已成为一种很有前景的认知评估工具。眼动的细微变化可能作为MCI的早期指标。然而，眼动追踪数据的解读具有挑战性。本研究引入了一种痴呆症筛查工具——虚拟现实眼动追踪认知评估（VECA），利用眼动追踪技术、机器学习和虚拟现实（VR），提供一种能够大规模部署的非侵入性、高效替代方法。对来自深圳宝安慢性病医院的201名参与者进行了VECA测试，利用通过VR头显捕捉的眼动数据来预测MoCA分数，并对不同教育背景下的认知障碍进行分类。所采用的支持向量回归模型与MoCA分数具有高度相关性（0.9），显著优于基线模型。此外，它还确定了用于识别认知障碍的最佳截断分数，具有显著的敏感性（88.5%）和特异性（83%）。本研究强调了VECA作为一种便携式、高效的早期痴呆症筛查工具的潜力，突出了将眼动追踪技术、机器学习和VR整合到认知健康评估中的益处。
