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（最常见的痴呆症）的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）是痴呆症的前兆，目前是通过蒙特利尔认知评估（MoCA）等神经心理学测试来识别的，但这些测试不适合大规模筛查。眼球追踪技术捕捉并量化与认知行为相关的眼球运动，已成为一种有前途的认知评估工具。眼球运动的细微变化可以作为 MCI 的早期指标。然而，眼球追踪数据的解释具有挑战性。这项研究引入了一种痴呆症筛查工具——VR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）来提供一种能够大规模部署的非侵入性、高效的替代方案。 VECA 对来自深圳宝安慢性病医院的 201 名参与者进行了研究，利用 VR 耳机捕获的眼动追踪数据来预测 MoCA 分数并对不同教育背景的认知障碍进行分类。采用的支持向量回归模型表现出与 MoCA 分数的高度相关性 (0.9)，显着优于基线模型。此外，它还建立了识别认知障碍的最佳截止分数，具有显着的敏感性（88.5％）和特异性（83％）。这项研究强调了 VECA 作为一种便携式、高效的早期痴呆症筛查工具的潜力，强调了将眼动追踪技术、机器学习和 VR 整合到认知健康评估中的好处。