Detecting Cerebral Palsy in Neonatal Stroke Children: GNN-Based Detection Considering the Structural Organization of Basal Ganglia

As a long-term consequence of neonatal arterial ischaemic stroke (NAIS), the presence of cerebral palsy (CP) depends on the structural integrity of brain areas, especially of basal ganglia. Yet, it remains challenging to establish an early diagnosis of CP from a conventional structural MRI. In this study, we introduce a graph neural network-based classification for the recognition of NAIS children and mainly for the detection of children with CP among the NAIS ones. From the structural MRI of 68 children aged 7 years old and their corresponding segmentation of basal ganglia, we construct graphs where nodes represent structures, carrying on node and edge attributes structural information (volumes, distances). The classification accuracy achieved by the proposed method is of 86% for the detection of NAIS and of 89% for the detection of CP among neonatal stroke children.