Deep Learning Pose Estimation Model for Parkinsonism and Levodopa-Induced Dyskinesia

Diagnosing Parkinson's disease is one of the largest challenges healthcare systems face due to the absence of a specific test for the condition and symptoms varying widely from person to person. Designing an automated model to aid in early diagnosis would greatly contribute to solving this problem. Currently, diagnosis for PD relies on clinical evaluation which has an error rate of approximately 20%, indicating the urgent need for an automated system to be developed. Levodopa is used for the treatment of Parkinson's Disease (PD) but can lead to motor complications known as levodopa-induced dyskinesia (LID) when taken for too long. PD and LID are evaluated according to the Unified Parkinson's Disease Rating Scale (UPDRS) and Unified Dyskinesia Rating Scale (UDysRS) scales, respectively, which range from 0 to 4 (0-normal, 4-severely impaired). The tests are conducted by medical personnel and are very subjective. The goal of this project was to design an algorithm using deep learning for assessment of parkinsonism and LID using pose estimation. Two models were created: a regression model to predict the clinical rating from 0 to 4 and a classification model to determine whether the patient had PD or LID. During the feature extraction process, 32 features were extracted per joint trajectory including 15 kinematic, 16 spectral, and the convex hull of the movements. Then, the two neural network models were trained on these features to be able to predict their respective targets. The classification model achieved a mean F1-score greater than 0.8 and the regression model attained a root mean square error less than 0.550, proving that this project was a promising start in the venture to automate diagnosis of Parkinson's disease.