An accessible and efficient mobile eye-tracking application for community-based cognitive impairment screening in China
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Background Cognitive impairment (CI) screening remains critically inaccessible in resource-limited Chinese communities. Methods We developed a tablet-based Mobile Eye-Tracking Application (m-ETA) using a three-step approach. A dementia discrimination model based on six oculometric features was trained in a hospital cohort (N = 204) and validated for biological relevance with Alzheimer's biomarkers (N = 101). Generalizability and accuracy were further assessed in a community cohort (N = 433) and two real-world populations (N = 2,685). Results m-ETA achieved high diagnostic accuracy for dementia (AUC = 0.99). The oculometric features were significantly associated with cognitive performance, brain atrophy, and tau deposition (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.