Discovering Causality Representations in Graph Convolutional Network for Automated Assessment of Parkinsonian Hand...

Postural tremor of hands, a typical symptom of Parkinson's disease (PD), is assessed using the Movement Disorder Society-sponsored revision of the Unified PD Rating Scale (MDS-UPDRS). However, the manual assessment process is both subjective and time-consuming, underscoring the necessity for automated rating models in clinical practice. To accommodate large-scale applications, such models must effectively identify and mitigate the impact of confounding factors in complex clinical settings, ensuring stable and robust assessment performance. Therefore, we propose a causality-representation graph convolutional network (GCN) scheme for automated video-based assessment of postural tremor of hands. This scheme systematically eliminates the effects of confounding factors by extracting discriminative structure and representation from hand skeleton graphs, which are clinically significant and causally related to tremor assessment, ultimately achieving stable multiclass tremor scoring. Specifically, a causality-informed graph structure mining (CI-GSM) module was first proposed to automatically extract nodes with discriminative tremor features from skeletal graphs, construct hand skeleton graphs causally related to tremor assessment, and thereby suppress interference from irrelevant nodes. Afterward, a causality-representation enhancement (CRE) module was designed to improve the construction of graph representations that correlate strongly with tremor assessment, thereby further reducing noise during evaluation. Our method achieves an accuracy of 64.01% and an acceptable accuracy of 98.62% on a large clinical dataset, demonstrating satisfactory performance on independent test sets collected from multiple centers. In conclusion, our proposed approach offers a convenient and stable solution for objective assessment of PD-associated tremors and holds significant potential for large-scale applications in remote PD assessment.