Multi Modal Deep Learning Diagnosis of Parkinson’s disease—A Systematic Review

Parkinson’s Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-the-art performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.