Eye movement combined machine learning methods for three‐classification diagnosis of Alzheimer's disease
Jiaqi Song, Weihua Yu, Yang Lü · 2025
Read Eye movement combined machine learning methods for three‐classification diagnosis of Alzheimer's disease by Jiaqi Song, Weihua Yu, Yang Lü (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11714644.txt.
Description
Jiaqi Song, Weihua Yu, and Yang Lu report a 2025 study that uses eye movement to help sort three levels of thinking health. The levels are normal skills, mild loss, and dementia from Alzheimer's disease. Telling these apart with machine learning has been hard to do well. The team recorded eye data from two hundred and fifty-eight people across the three groups. They then trained several kinds of model on the eye measures.
The best model sorted the three groups with close to seven in ten people placed right. Five eye measures mattered most for the sorting. Those measures also tracked with test scores for focus, memory, word use, and daily tasks. The authors say eye tracking is fast and easy to run in a clinic. It could serve as a helpful extra tool, not a stand-alone test, for doctors.
Catalog record
| CREATOR | Jiaqi Song, Weihua Yu, Yang Lü |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |