Abstract

The advancement of computer vision , together with increased usage of video cameras and medical images in clinical settings, has facilitated more advanced technology in movement and associated disorders. Many studies have been conducted to provide affordable methods for the diagnosis and treatment of movement disorders, including Tourette syndrome, Parkinson’s disease (PD), ataxia, and dyskinesia. According to the Parkinson's Foundation the disease affects about 10 million individuals globally. Traditional methods for diagnosing movement disorders typically rely on the subjective assessment of motor symptoms. Furthermore, overlapping symptoms among diseases may make early identification more challenging, and early signs are often missed. The ability of deep learning (DL) algorithms to analyze high-dimensional and complex data, such as genetic information, wearable sensor data, and medical imaging, has led to an increase in their usage in the diagnosis of PD and other movement disorders. Various DL methods, especially convolutional neural networks, are used to analyze brain imaging data including electroencephalogram (EEG) and electromyography (EMG) to identify irregularities in brain waves and muscle activity. DL also involves analyzing brain imaging data such as magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography scans to identify structural and functional abnormalities. Many important challenges remain, such as the need for personalized treatment, early and accurate diagnosis, distinguishing between similar conditions, better understanding of disease pathology, identifying and measuring symptoms, and predicting how each patient’s illness will progress. This chapter highlights how DL methods can help address these issues in PD and other movement disorders. These techniques support clinicians by improving screening, detection, and diagnosis, which in turn leads to increased patient care and treatment outcomes.