Early Detection of Alzheimer's Disease Through Eye Movement Analysis: A Digital Diagnostic Approach

Given the increasing worldwide incidence of dementia, primarily Alzheimer's disease dementia, there is an urgent requirement for non-invasive, early diagnostic techniques. This study introduces a novel approach utilizing eye movement analysis to detect early signs of cognitive decline indicative of dementia disorders. By analyzing the eye movement patternsof 95 participants, including 31 individuals with dementia and 64 cognitively normal controlsubjects, during tasks involving picture description and reading activities, distinct eye movement characteristics were identified. Using the Tobii Eye Tracker 5 for precise tracking and ASUS tablets for display, the study leveraged advanced machine learning algorithms such as XGBoost, logistic regression, and deep neural networks (DNNs) to analyze data. Significanteye movement features differentiated people with dementia from controls, indicating potential as early biomarkers. This approach demonstrated that digital eye tracking technologies combined with machine learning could offer a rapid, cost-effective solution for early all causedementia diagnosis, promising substantial improvements in patient care and management strategies. The outcomes suggest the feasibility of this method in clinical settings, highlightingthe importance of further research with larger sample sizes to refine the diagnostic models.