Deep Learning Approaches in the Early Diagnosis of Parkinson's Disease

Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. PD is characterized by motor and non-motor symptoms. It is highly established that PD is mainly caused by the degeneration of dopamine (DA) producing neurons in the substantia nigra pars compacta of the midbrain leading to nigro-striatal pathway dysregulation. The diagnosis of PD is difficult since its symptoms are quite similar to those of other disorders and current assessments of symptoms have many limitations. Moreover, there are currently no effective biomarkers for diagnosing this condition or tracking its progression. Recently, digital technologies including artificial intelligence (AI) methods have emerged. Indeed, machine learning and deep learning models can help in the diagnosis and management of PD. Deep learning models have shown promising results in the diagnosis of PD even at the early stages of the disease. This chapter will discuss the potential role of deep learning methods in the early diagnosis of PD.