Handwriting Analysis for the Development of a system to support the Diagnosis of Neurodegenerative Diseases

The prevalence of neurodegenerative diseases has been steadily increasing in recent years, underscoring a concerning trend. A rising number of individuals are facing these debilitating conditions, reflecting the complex challenges posed by ageing populations and changing lifestyles. The growth in the incidence of neurodegenerative diseases highlights the pressing need for continued research, heightened awareness, and enhanced support systems to address the evolving healthcare landscape and provide better care for those affected by these conditions. Another aspect underscoring the importance of research in this field is that neurodegenerative diseases currently lack a cure. They can cause cognitive impairments manifesting as difficulties in memory, language, thinking, judgment, and motor skills. Individuals exhibiting a combination of these symptoms face a significantly heightened risk of developing dementia and, in more severe cases, Alzheimer’s Disease (AD) or Parkinson’s Disease (PD). Given the progressive nature of AD, early detection becomes crucial to initiate therapies to mitigate its effects. Early diagnosis is a fundamental prerequisite for the effectiveness of these treatments, aimed at slowing down disease progression. This early intervention not only helps extend the life expectancy of patients but also enhances their overall quality of life. Once the signs of the disease manifest, substantial and irreversible damage may have already occurred. The impact of neurodegenerative diseases on handwriting is a notable concern, as these conditions can compromise fine motor control and cognitive functions. Individuals affected by neurodegenerative diseases often experience changes in their handwriting, such as altered penmanship, irregular letter shapes, and diminished overall legibility. This decline in handwriting proficiency can be studied as a tangible manifestation of the broader cognitive challenges associated with these diseases. Diagnosing neurodegenerative diseases like AD involves a comprehensive physician assessment, using several sources of information and incorporating various tools and tests to evaluate cognitive function, neurological health, and overall well-being. The involvement of Artificial Intelligence (AI) in supporting the diagnosis of neurodegenerative diseases has been a progressively evolving field over the past couple of decades. The application of AI techniques gained momentum in the 21st century with advances in computational power, the availability of data, and improvements in algorithmic approaches. Since the early to mid-2000s, researchers began exploring the potential of AI in analyzing various data types associated with neurodegenerative diseases, including medical images, genetic information, and clinical data. The use of AI in supporting the diagnosis of Neurodegenerative Diseases (NDs) represents a promising frontier in healthcare. AI applications, such as machine learning algorithms and deep learning models, analyze vast datasets to identify patterns and indicators associated with neurodegenerative conditions. These technologies offer the potential for earlier and more accurate detection of NDs, facilitating timely intervention and personalized treatment plans. In the past ten years, the research community agreed that the application of artificial intelligence to handwriting analysis holds great potential for supporting the diagnosis of NDs. AI algorithms can discern subtle changes in handwriting patterns, offering valuable insights into cognitive decline associated with conditions like AD or PD. By analyzing features such as pressure, speed, and stroke dynamics, AI may contribute to the early detection and monitoring of NDs, providing a non-invasive and cost-effective diagnostic tool. This approach enables a better understanding of neurological changes and complements traditional diagnostic methods. Ongoing research in AI-driven handwriting analysis underscores its promise in enhancing the accuracy and efficiency of NDs diagnosis.