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Postoperative infection diagnosis is a common and serious complication thatgenerally poses a high diagnostic challenge. This study focuses on PJI, a typeof postoperative infection. X-ray examination is an imaging examination forsuspected PJI patients that can evaluate joint prostheses and adjacent tissues,and detect the cause of pain. Laboratory examination data has high sensitivityand specificity and has significant potential in PJI diagnosis. In this study,we proposed a self-supervised masked autoencoder pre-training strategy and amultimodal fusion diagnostic network MED-NVC, which effectively implements theinteraction between two modal features through the feature fusion network ofCrossAttention. We tested our proposed method on our collected PJI dataset andevaluated its performance and feasibility through comparison and ablationexperiments. The results showed that our method achieved an ACC of 94.71% andan AUC of 98.22%, which is better than the latest method and also reduces thenumber of parameters. Our proposed method has the potential to provideclinicians with a powerful tool for enhancing accuracy and efficiency.

A multimodal method based on cross-attention and convolution for postoperative infection diagnosis