Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

Can Gait Features Help in Differentiating Parkinson’s Disease Medication States and Severity Levels? A Machine Learning Approach

“…However, there is a limitation in the confidence of the estimations obtained when only gait analysis is explored. We reaffirm recent findings, [65,66], that a more robust approach should include the integration of motor symptoms from lower and upper extremities and other non-motor symptoms, such as depression, dysarthria, and cognitive impairment.…”

Early Diagnosis of Parkinson’s Disease: Utility of Animal Models

“…Several models focused on pre-motor symptoms of the disease and can predict the disease condition with acceptable accuracy. There is a need to tune the models with more data and play with other parameters and features, deploy other ML algorithms to improve the accuracy of the disease early diagnosis [15].…”

Preclinical reserpine models recapitulating motor and non-motor features of Parkinson’s disease: Roles of epigenetic upregulation of alpha-synuclein and autophagy impairment

“…Parkinson’s disease (PD) is a common neurodegenerative disease affecting more than six million people worldwide. PD is one of the main reasons for disability globally, and its incidence is increasing at a rate faster than that of other neurological diseases ( Loh et al, 2021 ). The prevalence of PD increases with age, reaching its peak between 85 and 89 years old.…”