Handwriting as a Biomarker for Early Detection of Parkinson’s and Alzheimer’s Diseases: A Comprehensive Guide for Researchers

Neurodegenerative diseases such as Parkinson’s disease (PD) and Alzheimer’s disease (AD) present a significant and growing challenge to the healthcare systems worldwide. Both conditions are progressive and often undetected early, making timely diagnosis crucial. Recently, breakthroughs in computer vision and artificial intelligence have enabled the development of non-invasive and cost-effective screening and decision-making tools, allowing for earlier detection of the disease. This review serves as a comprehensive guide, providing structured insights into computational research methods for automated detection of PD and AD, with focus on handwriting analysis as a subtle behavioral biomarker of neurological impairment. A range of methodologies is examined, including static and dynamic handwriting assessment, feature engineering procedures, deep learning and classical ML-based approaches. The analysis emphasizes the most effective methods, the handwriting features found to be most revealing, the datasets most used in the literature, and the performance levels reported for each disease. Several studies report that transfer learning based on convolutional neural networks and transformer-based architectures for Parkinson’s disease diagnosis is regularly able to achieve high accuracy, frequently above 95% on benchmark datasets. In contrast, Alzheimer’s disease research is progressively benefitting from multimodal approaches combining kinematic and spatial handwriting features to capture cognitive and motor changes. Structured summaries of publicly available handwriting datasets are provided, and critical advancements, ongoing challenges, and future research priorities are discussed. The integration of insights across the studies, through this work, aims to assist researchers and clinicians in the development and translation of handwriting-based, AI-guided diagnostic tools for neurodegenerative diseases.