A Methodological and Structural Review of Parkinson’s Disease Detection Across Diverse Data Modalities

Parkinson’s Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally—1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation—early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. Notably, limitations such as the lack of robust multimodal frameworks and challenges in continuous PD recognition are identified, underscoring the need for innovative solutions. The review highlights significant advancements in PD recognition, particularly the transition from handcrafted feature engineering to DL-based methods. Furthermore, it explores promising trends in multimodal approaches, offering insights into overcoming existing challenges such as data scarcity, model generalization, and computational efficiency. The study emphasizes the importance of integrating diverse data sources to achieve higher diagnostic accuracy and reliability. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next-generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.