Assessment of Parkinson's Disease Severity from Videos Using Deep Architectures

Assessment of Parkinson's Disease Severity from Videos Using Deep Architectures

Yin, Zhao (Student TU Delft)
Geraedts, Victor Jacobus (Leiden University Medical Center)
Wang, Z. (TU Delft Pattern Recognition and Bioinformatics)
Contarino, Maria Fiorella (Leiden University Medical Center)
Dibeklioglu, H. (TU Delft Pattern Recognition and Bioinformatics)
van Gemert, J.C. (TU Delft Pattern Recognition and Bioinformatics)

2022

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

deep learning
Diseases
Feature extraction
multi-domain learning
Parkinson's disease (PD)
self-attention
severity classification
Task analysis
Three-dimensional displays
Training
Transfer learning
transfer learning
Videos

http://resolver.tudelft.nl/uuid:e7080c12-62ba-4f9e-932a-99d921743365

https://doi.org/10.1109/JBHI.2021.3099816

2168-2194

IEEE Journal of Biomedical and Health Informatics, 26 (3), 1164-1176

Institutional Repository

journal article

© 2022 Zhao Yin, Victor Jacobus Geraedts, Z. Wang, Maria Fiorella Contarino, H. Dibeklioglu, J.C. van Gemert