RESUMO
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in cognitive decline, dementia, and eventually death. Diagnosing early signs of AD can help clinicians to improve the quality of life. OBJECTIVE: We developed a non-invasive approach to help neurologists and clinicians to distinguish probable AD patients and healthy controls (HC). METHODS: The patients' gaze points were followed based on the words they used to describe the Cookie Theft (CT) picture description task. We hypothesized that the timing of words enunciation aligns with the participant's eye movements. The moments that each word was spoken were then aligned with specific regions of the image. We then applied machine learning algorithms to classify probable AD and HC. We randomly selected 60 participants (30 AD and 30 HC) from the Dementia Bank (Pitt Corpus). RESULTS: Five main classifiers were applied to different features extracted from the recorded audio and participants' transcripts (AD and HC). Support vector machine and logistic regression had the highest accuracy (up to 80% and 78.33%, respectively) in three different experiments. CONCLUSIONS: In conclusion, point-of-gaze can be applied as a non-invasive and less expensive approach compared to other available methods (e.g., eye tracker devices) for early-stage AD diagnosis.