bibtext: @InProceedings{ Qua_CausalityInformed_MICCAI2024,
                   author = { Quan, Yuyang and Zhang, Chencheng and Guo, Rui and Qian, Xiaohua },
                   title = { { Causality-Informed Fusion Network for Automated Assessment of Parkinsonian Body Bradykinesia } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Body bradykinesia, a prominent clinical manifestation of Parkinson’s disease (PD), characterizes a generalized slowness and diminished movement across the entire body. The assessment of body bradykinesia in the widely employed PD rating scale (MDS-UPDRS) is inherently subjective, relying on the examiner’s overall judgment rather than specific motor tasks. Therefore, we propose a graph convolutional network (GCN) scheme for automated video-based assessment of parkinsonian body bradykinesia. This scheme incorporates a causality-informed fusion network to enhance the fusion of causal components within gait and leg-agility motion features, achieving stable multi-class assessment of body bradykinesia. Specifically, an adaptive causal feature selection module is developed to extract pertinent features for body bradykinesia assessment, effectively mitigating the influence of non-causal features. Simultaneously, a causality-informed optimization strategy is designed to refine the causality feature selection module, improving its capacity to capture causal features. Our method achieves 61.07% accuracy for three-class assessment on a dataset of 876 clinical case. Notably, our proposed scheme, utilizing only consumer-level cameras, holds significant promise for remote PD bradykinesia assessment.
title: Causality-Informed Fusion Network for Automated Assessment of Parkinsonian Body Bradykinesia
authors: Quan, Yuyang Zhang, Chencheng Guo, Rui Qian, Xiaohua