Early Alzheimer's disease diagnosis via handwriting with self-attention mechanisms

The neurodegenerative diseases like Alzheimer's disease (AD) can result in progressive decline in both cognitive functions and motor skills, which have critical need for accurate early diagnosis. However, current diagnosis approaches primarily rely on timely clinical magnetic resonance imaging (MRI) scans, which impede widely application for potential patients. Leveraging handwriting as a diagnostic tool offers significant potential for identifying AD in its early stages.

This study aims to develop an efficient, rapid, and accurate method for early diagnosis of AD by utilizing handwriting analysis, a promising avenue due to its association with compromised motor skills in neurodegenerative diseases.

We propose a novel methodology that leverages self-attention mechanisms for the early diagnosis of AD. Our approach integrates data from 25 distinct handwriting tasks available in the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset.

The Self-Attention model achieved an accuracy of 94.3% and an F1-score of 94.5%, outperforming other state-of-the-art models, including traditional machine learning and deep learning approaches. Specially, the Self-Attention model surpassed the previous best model, the convolutional neural networks, by approximately 4% in both accuracy and F1-score. Additionally, the model demonstrated superior precision (94.7%), sensitivity (94.5%), and specificity (94.1%), indicating high reliability and excellent identification of true positive and true negative cases, which is crucial in medical diagnostics.

Handwriting analysis, powered by self-attention mechanisms, offers significant potential as a diagnostic tool for identifying AD in its early stages, providing an effective alternative to traditional MRI-based diagnosis.