A Causality-Informed Graph Convolutional Network for Video Assessment of Parkinsonian Leg Agility

Leg agility is a key indicator of bradykinesia, which in turn is a cardinal manifestation of Parkinson’s disease (PD). In fact, automated video assessment of the leg-agility task is critically required for improving the efficiency and objectivity of PD diagnosis. Therefore, we propose a causality-informed graph convolutional network to extract discriminative clinically-meaningful motion features from human skeletons in videos, finally achieving stable leg-agility 5-point scoring. The proposed scheme systematically mines causal features of each skeleton graph from graph node, structure, and representation levels. Specifically, we firstly developed a causality-informed node selection mechanism to mine the graph nodes representing the discriminative features, and thus identify nodes causally correlated to the clinical assessment aspects and suppress the interference from other nodes. Afterwards, a causality-informed structure generation mechanism was designed to generate a graph structure encoding the connections between the discriminative nodes, hence maintaining the discriminability of features associated with these causality-informed nodes. Finally, we employed a clinically-driven self-supervised learning scheme to embed clinical prior knowledge into the proposed model and hence boost the clinical significance of the causality-informed graph nodes, structures, and representations. The proposed method achieved a 71.11% accuracy and a 98.93% acceptable accuracy on a large clinical video dataset. Its effectiveness was also confirmed on an independent test set, and the obtained results exhibited interpretability from modeling and clinical perspectives. In conclusion, our method provides a highly stable scheme for objective video quantification of bradykinesia. Our source code will be released at https://github.com/SJTUBME-QianLab/PD-CIGCN.