Multimodal Machine Learning and Deep Learning Approaches for Parkinson's Disease Diagnosis: A Comprehensive Survey.

Area of Science:

• Neurology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting patient quality of life.
• Early and accurate diagnosis of PD is crucial for effective intervention and improved patient outcomes.
• Traditional clinical evaluations for PD can be subjective and may lack precision.

Purpose of the Study:

• To review recent advancements in machine learning (ML) and deep learning (DL) for PD diagnosis.
• To explore the application of ML/DL techniques across various data sources for objective PD assessment.
• To identify challenges and future directions in AI-driven PD diagnosis.

Main Methods:

• Systematic review of ML and DL techniques applied to PD diagnosis.
• Analysis of studies utilizing electroencephalogram (EEG) signals, voice recordings, and Magnetic Resonance Imaging (MRI) data.
• Evaluation of the effectiveness and limitations of different AI models.

Main Results:

• ML and DL show promise for more objective and quantitative PD diagnosis.
• AI models demonstrate effectiveness when applied to EEG, voice, and MRI data.
• Current challenges include data limitations, model interpretability, and generalizability.

Conclusions:

• AI, particularly ML and DL, offers a powerful toolkit for enhancing PD diagnosis.
• Future research should focus on explainable AI and multimodal data integration for robust clinical application.
• Overcoming data and interpretability challenges is key to widespread adoption of AI in PD diagnostics.