Periprosthetic joint infection (PJI) is a severe complication after joint replacement surgery that demands precise diagnosis for effective treatment. We enhanced PJI diagnostic accuracy through three steps: (1) developing a self-supervised PJI model with DINO v2 to create a large dataset; (2) comparing multiple intelligent models to identify the best one; and (3) using the optimal model for visual analysis to refine diagnostic practices. The self-supervised model generated 27,724 training samples and achieved a perfect AUC of 1, indicating flawless case differentiation. EfficientNet v2-S outperformed CAMEL2 at the image level, while CAMEL2 was superior at the patient level. By using the weakly supervised PJI model to adjust diagnostic criteria, we reduced the required high-power field diagnoses per slide from five to three. These findings demonstrate AI’s potential to improve the accuracy and standardization of PJI pathology and have significant implications for infectious disease diagnostics.

假体周围关节感染 （PJI） 是关节置换手术后的一种严重并发症，需要精确诊断才能进行有效治疗。我们通过三个步骤提高了 PJI 诊断的准确性：（1） 使用 DINO v2 开发一个自我监督的 PJI 模型，以创建一个大型数据集;（2） 比较多个智能模型以确定最佳模型;（3） 使用最佳模型进行可视化分析以改进诊断实践。自我监督模型生成了 27,724 个训练样本，并实现了完美的 AUC 1，表明大小写区分完美无瑕。EfficientNet v2-S 在图像层面优于 CAMEL2，而 CAMEL2 在患者层面更胜一筹。通过使用弱监督 PJI 模型来调整诊断标准，我们将每张载玻片所需的高功率视野诊断从 5 次减少到 3 次。这些发现证明了人工智能在提高 PJI 病理学的准确性和标准化方面的潜力，并对传染病诊断具有重要意义。