Zhan Hongwei, Zhao Zandong, Liang Qiuzhen, Zheng Jiang, Zhang Liang
Department of Sports Medicine, Honghui Hospital, Xi'an Jiaotong University, 555 Youyi East Road, Nanshaomen, Beilin District, Xi'an, Shaanxi, China.
J Orthop Surg Res. 2025 Sep 26;20(1):847. doi: 10.1186/s13018-025-06247-4.
The evaluation of patellofemoral joint parameters is essential for diagnosing patellar dislocation, yet manual measurements exhibit poor reproducibility and demonstrate significant variability dependent on clinician expertise. This systematic review aimed to evaluate the performance of artificial intelligence (AI) models in automatically measuring patellofemoral joint parameters.
A comprehensive literature search of PubMed, Web of Science, Cochrane Library, and Embase databases was conducted from database inception through June 15, 2025. Two investigators independently performed study screening and data extraction, with methodological quality assessment based on the modified MINORS checklist. This systematic review is registered with PROSPERO. A narrative review was conducted to summarize the findings of the included studies.
A total of 19 studies comprising 10,490 patients met the inclusion and exclusion criteria, with a mean age of 51.3 years and a mean female proportion of 56.8%. Among these, six studies developed AI models based on radiographic series, nine on CT imaging, and four on MRI. The results demonstrated excellent reliability, with intraclass correlation coefficients (ICCs) ranging from 0.900 to 0.940 for femoral anteversion angle, 0.910-0.920 for trochlear groove depth and 0.930-0.950 for tibial tuberosity-trochlear groove distance. Additionally, good reliability was observed for patellar height (ICCs: 0.880-0.985), sulcus angle (ICCs: 0.878-0.980), and patellar tilt angle (ICCs: 0.790-0.990). Notably, the AI system successfully detected trochlear dysplasia, achieving 88% accuracy, 79% sensitivity, 96% specificity, and an AUC of 0.88.
AI-based measurement of patellofemoral joint parameters demonstrates methodological robustness and operational efficiency, showing strong agreement with expert manual measurements. To further establish clinical utility, multicenter prospective studies incorporating rigorous external validation protocols are needed. Such validation would strengthen the model's generalizability and facilitate its integration into clinical decision support systems.
This systematic review was registered in PROSPERO (CRD420251075068).
评估髌股关节参数对于诊断髌骨脱位至关重要，但手动测量的可重复性较差，且因临床医生的专业水平不同而存在显著差异。本系统评价旨在评估人工智能（AI）模型自动测量髌股关节参数的性能。
从数据库建立至2025年6月15日，对PubMed、Web of Science、Cochrane图书馆和Embase数据库进行了全面的文献检索。两名研究人员独立进行研究筛选和数据提取，并根据修改后的MINORS清单进行方法学质量评估。本系统评价已在PROSPERO注册。进行叙述性综述以总结纳入研究的结果。
共有19项研究，涉及10490例患者，符合纳入和排除标准，平均年龄51.3岁，女性平均比例为56.8%。其中，6项研究基于X线片系列开发AI模型，9项基于CT成像，4项基于MRI。结果显示可靠性极佳，股骨前倾角的组内相关系数（ICC）为0.900至0.940，滑车沟深度为0.910 - 0.920，胫骨结节 - 滑车沟距离为0.930 - 0.950。此外，髌骨高度（ICC：0.880 - 0.985）、沟角（ICC：0.878 - 0.980）和髌骨倾斜角（ICC：0.790 - 0.990）也具有良好的可靠性。值得注意的是，AI系统成功检测到滑车发育不良，准确率达88%，灵敏度为79%，特异度为96%，曲线下面积（AUC）为0.88。
基于AI测量髌股关节参数显示出方法的稳健性和操作效率，与专家手动测量结果高度一致。为进一步确立临床实用性，需要开展纳入严格外部验证方案的多中心前瞻性研究。这种验证将增强模型的可推广性，并促进其融入临床决策支持系统。
本系统评价已在PROSPERO注册（CRD420251075068）。
