Timely Identification of Parkinson's Disease

Abstract

This paper presents a novel approach aimed at the early identification of Parkinson's disease through the integration of Artificial Intelligence (AI) and analysis of spiral sketches. In the context of healthcare, where early diagnosis is paramount, leveraging AI to analyze spiral sketches provides a non-invasive and accessible method for identifying potential Parkinson's cases. In contrast to traditional diagnostic methods, which may lack sensitivity in the early stages of the disease, the system focuses on a user-friendly interface, allowing individuals to draw spirals. Advanced machine learning methodologies are utilized to conduct an extensive examination of these sketches, furnishing valuable insights into the probability of Parkinson's disease. Real-time analysis is prioritized in this solution, enabling prompt assessment of Parkinson’s symptoms in spiral sketches. The future vision includes enhancements such as multimodal analysis, telemedicine, integration, and longitudinal monitoring and progress tracking. Ethical considerations, regular model training and integration with the healthcare systems are important aspects of the system. These not only ensure responsible AI practices but also provide actionable insights for effective disease management. Empowering individuals and healthcare professionals, the project facilitates informed decision-making based on evolving trends in spiral sketch analysis in the realm of Parkinson's disease detection.