Enhancing Alzheimer’s detection: VAE-augmented handwriting analysis

Early detection of Alzheimer’s Disease (AD) is pivotal for timely intervention and management, yet it remains a challenging endeavor due to data scarcity and privacy concerns. Existing methods leveraging handwriting analysis have shown promise, but are hindered by the lack of extensive public datasets and the sensitivity of personal medical data. This study introduces an innovative approach to address these challenges using Variational Autoencoders (VAEs) for data augmentation and anonymization. Leveraging the limited DARWIN handwriting dataset, VAEs generate synthetic handwriting samples, expanding the dataset and enhancing machine learning classifier training. This approach improves detection accuracy and ensures data privacy by eliminating the need for direct personal data. Our methodology involves augmenting handwriting samples from both healthy individuals and those with AD using VAEs, creating a robust dataset for training machine learning models to identify early AD patterns. Results show significant accuracy improvements, demonstrating the potential of VAE-augmented handwriting analysis as a non-invasive, privacy-preserving tool for early AD diagnosis. This study offers a novel solution to data scarcity in medical diagnostics.