Zhang Zhihong, Hui Xu, Tao Huimin, Fu Zhenjiang, Cai Zaili, Zhou Sheng, Yang Kehu
Department of The First Clinical Medical College of Gansu, University of Chinese Medicine, Lanzhou, Gansu, China.
Department of Evidence-Based Medicine Centre, School of Basic Medical Science, Lanzhou University, Lanzhou, Gansu, China.
PLoS One. 2025 May 7;20(5):e0321104. doi: 10.1371/journal.pone.0321104. eCollection 2025.
Artificial intelligence (AI) is a promising and powerful technology with increasing use in orthopedics. The global morbidity of knee arthroplasty is expanding. This study investigated the use of AI algorithms to review radiographs of knee arthroplasty.
The Ovid-Embase, Web of Science, Cochrane Library, PubMed, China National Knowledge Infrastructure (CNKI), WeiPu (VIP), WanFang, and China Biology Medicine (CBM) databases were systematically screened from inception to March 2024 (PROSPERO study protocol registration: CRD42024507549). The quality assessment of the diagnostic accuracy studies tool assessed the risk of bias.
A total of 21 studies were included in the analysis. Of these, 10 studies identified and classified implant brands, 6 measured implant size and component alignment, 3 detected implant loosening, and 2 diagnosed prosthetic joint infections (PJI). For classifying and identifying implant brands, 5 studies demonstrated near-perfect prediction with an area under the curve (AUC) ranging from 0.98 to 1.0, and 10 achieved accuracy (ACC) between 96-100%. Regarding implant measurement, one study showed an AUC of 0.62, and two others exhibited over 80% ACC in determining component sizes. Moreover, Artificial intelligence showed good to excellent reliability across all angles in three separate studies (Intraclass Correlation Coefficient > 0.78). In predicting PJI, one study achieved an AUC of 0.91 with a corresponding ACC of 90.5%, while another reported a positive predictive value ranging from 75% to 85%. For detecting implant loosening, the AUC was found to be at least as high as 0.976 with ACC ranging from 85.8% to 97.5%.
These studies show that AI is promising in recognizing implants in knee arthroplasty. Future research should follow a rigorous approach to AI development, with comprehensive and transparent reporting of methods and the creation of open-source software programs and commercial tools that can provide clinicians with objective clinical decisions.
人工智能（AI）是一项很有前景且功能强大的技术，在骨科领域的应用日益广泛。膝关节置换术的全球发病率正在上升。本研究调查了使用人工智能算法来评估膝关节置换术的X线片。
对Ovid-Embase、Web of Science、Cochrane图书馆、PubMed、中国知网（CNKI）、维普（VIP）、万方和中国生物医学数据库（CBM）从建库至2024年3月进行系统筛选（PROSPERO研究方案注册号：CRD42024507549）。诊断准确性研究工具的质量评估用于评估偏倚风险。
分析共纳入21项研究。其中，10项研究识别并分类了植入物品牌，6项测量了植入物尺寸和部件对线情况，3项检测了植入物松动，2项诊断了人工关节感染（PJI）。对于植入物品牌的分类和识别，5项研究显示预测近乎完美，曲线下面积（AUC）范围为0.98至1.0，10项研究的准确率（ACC）在96%至100%之间。关于植入物测量，一项研究的AUC为0.62，另外两项研究在确定部件尺寸方面的ACC超过80%。此外，在三项独立研究中，人工智能在所有角度均显示出良好至优异的可靠性（组内相关系数>0.78）。在预测PJI方面，一项研究的AUC为0.91，相应的ACC为90.5%，而另一项研究报告的阳性预测值在75%至85%之间。对于检测植入物松动，发现AUC至少高达0.976，ACC范围为85.8%至97.5%。
这些研究表明，人工智能在识别膝关节置换术中的植入物方面很有前景。未来的研究应采用严谨的人工智能开发方法，全面、透明地报告方法，并创建开源软件程序和商业工具，为临床医生提供客观的临床决策。
