  Prosthetic Joint Infection (PJI) is a prevalent and severe complication
characterized by high diagnostic challenges. Currently, a unified diagnostic
standard incorporating both computed tomography (CT) images and numerical text
data for PJI remains unestablished, owing to the substantial noise in CT images
and the disparity in data volume between CT images and text data. This study
introduces a diagnostic method, HGT, based on deep learning and multimodal
techniques. It effectively merges features from CT scan images and patients'
numerical text data via a Unidirectional Selective Attention (USA) mechanism
and a graph convolutional network (GCN)-based feature fusion network. We
evaluated the proposed method on a custom-built multimodal PJI dataset,
assessing its performance through ablation experiments and interpretability
evaluations. Our method achieved an accuracy (ACC) of 91.4\% and an area under
the curve (AUC) of 95.9\%, outperforming recent multimodal approaches by 2.9\%
in ACC and 2.2\% in AUC, with a parameter count of only 68M. Notably, the
interpretability results highlighted our model's strong focus and localization
capabilities at lesion sites. This proposed method could provide clinicians
with additional diagnostic tools to enhance accuracy and efficiency in clinical
practice.
