Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey

Highly adaptable and reusable AI models are revolutionizing the diagnosis and management of Parkinson's disease (PD). A wide range of AI algorithms, including machine learning and deep learning techniques, are now being employed for PD diagnosis and treatment. These algorithms leverage multimodal data, such as gait patterns, hand movements, and speech characteristics, to predict the likelihood of PD, evaluate symptom severity, facilitate early detection, and assess the effectiveness of treatments, demonstrating their advanced diagnostic potential. This paper provides a comprehensive review of machine learning and deep learning approaches for PD detection and assessment over the past decade, emphasizing their strengths, addressing their limitations, and exploring their potential to inspire new research directions. Additionally, it offers a curated collection of publicly available multimodal datasets focused on PD motor symptoms and validates the performance of these algorithms on privately collected datasets. Experimental results reveal that AI technologies for PD assessment have progressed from traditional methods to convolutional and sequential neural networks, and further to Transformer-based models, achieving consistently improving accuracy and establishing benchmarks for future advancements.