alzheimers
Can eye tracking plus deep learning flag Alzheimer’s?
Open access · cc by · source: Europe PMC
On a 210-person 3D visuospatial eye-tracking set, a nested-autoencoder model reached 85% average accuracy distinguishing AD from healthy controls.
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Can eye tracking plus deep learning flag Alzheimer’s?
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108 AD patients and 102 healthy controls were recruited.
A total of 108 patients with AD and 102 healthy controls were recruited from September 2020 to September 2021
Proposed model showed 85% average AD recognition accuracy.
The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks
Key findings
The proposed network averaged 85% AD-recognition accuracy and beat the comparison ML/DL baselines on the same eye-tracking dataset.
Methodology
Built a 3D visuospatial memory task, recorded eye movements from 108 AD patients and 102 controls, extracted fixation heatmaps, and trained a nested autoencoder with adaptive feature fusion, comparing against traditional ML and typical DL models via four-fold CV (plus ablations).
Limitations
Single-center Chinese clinic sample; 85% is cross-validated accuracy, not a deployed screening claim, and does not replace clinical diagnosis.
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