Deep Learning for Parkinson’s Disease Diagnosis


Parkinson’s disease (PD), is a neurodegenerative disorder, whose symptoms (tremor, rigidity, and intentional movements) negatively affect the patient’s daily lives. While there is no cure for PD, early diagnosis and proper treatment are of great importance in easing the patient from symptoms and, in turn, improving the quality of daily life. Analysis of drawing and handwriting skills is one of the procedures widely used in the diagnostics of PD. Deep learning has achieved remarkable results in the medical field, such as the diagnosis, segmentation, prediction, and detection of various anatomical regions of interest (ROI). Artificial intelligence-based approaches could help specialists and physicians in supporting the diagnosis of PD.

Name of Researcher: Xuechao Wang, Junqing Huang, Marianna Chatzakou

Scientist in Charge: Prof. Michael Ruzhansky, Dr. Sven Nõmm

Host: Ghent University, Tallinn University of Technology

In this project, our objective is to introduce deep learning to assist traditional methods of diagnosing Parkinson’s disease, specifically, to transform the collected handwriting exam data into different dimensions, to combine it with the appropriate deep learning models. The research project is divided into three parts:

Part I time-series data

In this part, we focus on extracting stationary signals that reflect the fine motor control abilities of the participants to highlight motion symptoms that are more pronounced in fine motor activity in patients with Parkinson’s disease…

Part II color-image data

In this part, we try to construct a 2D colour image from raw data, combined with the high fitting ability of 2D convolution kernels, to extract high-dimensional abstract features for the diagnosis of Parkinson’s disease…

Part III point-cloud data

In this part, we want to use raw data to build point cloud with RGB information in this part, and combine it with the appropriate deep learning models…


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