Keywords: Osteoarthritis, Machine Learning/Artificial IntelligenceIdentifying patients with knee osteoarthritis (OA) whom the disease will progress is critical in clinical practice. Currently, the time-series information and interactions between the structures and sub-regions of the whole knee are underused for predicting. Therefore, we propose a temporal-structural graph convolutional network (TSGCN) using time-series data of 194 cases and 406 OA comparators. Each sub-region was regarded as a vertex and represented by the extracted radiomics features, the edges between vertexs were established by the clinical prior knowledge. The multiple-modality TSGCN (integrating information of MRIs, clinical and image-based semi-quantitative score) performed best comparing to the radiomics and CNN model.
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