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

翻译：术后感染诊断是一种常见且严重的并发症，通常具有较高的诊断难度。本研究聚焦于PJI（一种术后感染类型）。X线检查是对疑似PJI患者进行的影像学检查，可评估关节假体及周围组织，并检测疼痛原因。实验室检查数据具有高敏感性和高特异性，在PJI诊断中具有显著潜力。本研究提出了一种自监督掩码自编码器预训练策略及多模态融合诊断网络MED-NVC，通过CrossAttention特征融合网络有效实现了两种模态特征间的交互。我们在自建的PJI数据集上测试了所提方法，并通过对比实验和消融实验评估其性能与可行性。结果表明，我们的方法达到了94.71%的ACC和98.22%的AUC，优于最新方法，同时减少了参数量。所提出的方法有望为临床医生提供提升准确性与效率的强力工具。