A Systematic Review of Deep Learning Applications in Parkinson's Disease Research

Parkinson's disease (PD) is a degenerative neurological disorder that impacts millions of individuals globally. In recent years, deep learning (DL) techniques have emerged as powerful tools to enhance the accuracy and efficiency of diagnosing and managing PD. This systematic review provides a comprehensive analysis of the various deep learning approaches applied to PD research, particularly in diagnostic and prognostic contexts. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, incorporating studies published up to the current year. The analysis focuses on key elements such as dataset quality, data preprocessing methods, feature extraction techniques, and model evaluation metrics. The survey identifies the most common deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have demonstrated promising results in PD diagnosis. Additionally, this review explores the limitations and challenges of current models and suggests potential pathways for future research, such as integrating multi-modal data and developing more generalized models for clinical use. The findings aim to establish a foundational understanding for further advancement of DL techniques in the early detection and comprehensive management of Parkinson's disease.

Keywords:

Parkinson's Disease , Deep Learning , Convolutional Neural Networks , Recurrent Neural Networks , Clinical Decision Support