Alzheimer's research & therapy · 2025
An accessible and efficient mobile eye-tracking application for community-based cognitive impairment screening in China.
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Wei Mingxia, Li Jincheng, You Tongyao, Yu Yu, Lu Jiaying, Liang Suzhen, Jin Zishuo, Han Qi, Zuo Chuantao, Ye Jianfeng, Yu Jintai, Chen Xingdong, Dong Qiang, Wu Wenwen, Wang Yingzhe, Jiang Yanfeng, Cui Mei
Аннотация
BACKGROUND: Cognitive impairment (CI) poses a major global health challenge. In China, neuropsychological scales, regarded as the gold standard for cognitive diagnosis, are largely inaccessible in resource-limited communities. The Mobile Eye-Tracking Application (m-ETA), which captures and quantifies eye movement features, has emerged as a promising tool for CI screening.
METHODS: We developed a tablet-based m-ETA using a two-step approach. First, a logistic regression (LR) model was trained to discriminate dementia based on six oculometric features in a hospital cohort (N = 204), and regression analyses were conducted to validate the biological relevance of these features with Alzheimer's Disease-related phenotypes in an exploratory dataset (N = 101). Second, the generalizability and accuracy of the LR model were externally validated in a community-based cohort (N = 433) and further evaluated in two real-world community populations (N = 2,685). Model performance was assessed using sensitivity, specificity, negative predictive value (NPV), and area under the ROC curve (AUC).
RESULTS: m-ETA achieved high diagnostic accuracy for dementia (AUC = 0.99). Regression analyses confirmed that the m-ETA-derived oculometric features were significantly associated with cognitive performance, brain atrophy, and tau deposition in the exploratory dataset (all P < 0.05). m-ETA accurately detected CI (AUC = 0.80), with excellent negative predictive value for ruling out CI, and identified individuals with lower cognition performance across diverse communities.
CONCLUSIONS: m-ETA offers a low-cost, non-invasive, and efficient tool for large-scale CI screening, particularly suited to underserved and low-literacy communities in China.
METHODS: We developed a tablet-based m-ETA using a two-step approach. First, a logistic regression (LR) model was trained to discriminate dementia based on six oculometric features in a hospital cohort (N = 204), and regression analyses were conducted to validate the biological relevance of these features with Alzheimer's Disease-related phenotypes in an exploratory dataset (N = 101). Second, the generalizability and accuracy of the LR model were externally validated in a community-based cohort (N = 433) and further evaluated in two real-world community populations (N = 2,685). Model performance was assessed using sensitivity, specificity, negative predictive value (NPV), and area under the ROC curve (AUC).
RESULTS: m-ETA achieved high diagnostic accuracy for dementia (AUC = 0.99). Regression analyses confirmed that the m-ETA-derived oculometric features were significantly associated with cognitive performance, brain atrophy, and tau deposition in the exploratory dataset (all P < 0.05). m-ETA accurately detected CI (AUC = 0.80), with excellent negative predictive value for ruling out CI, and identified individuals with lower cognition performance across diverse communities.
CONCLUSIONS: m-ETA offers a low-cost, non-invasive, and efficient tool for large-scale CI screening, particularly suited to underserved and low-literacy communities in China.