A Review on Radiomics and Machine Learning Algorithms in Prediction and Progression of Parkinson’s Disease

Parkinson's disease (PD) is an irreversible neurodegenerative condition that impairs dopamine production in a specific area of the brain. Diagnosis of PD in the early stage is a tedious task as symptoms are not very prominently shown in the first stage. Machine learning techniques had been used for the analysis and progression of PD. Radiomics analysis has helped in extracting large amount of quantifiable features from images which can be used as biomarkers for predictive models. This paper gives an idea on different machine learning algorithms and the application of radiomics which has proven its efficacy in the classification and progression of PD. Finally, the limitations and accuracy of the different techniques are also discussed.