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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p> Systematic review registration <p>This systematic review was registered in PROSPERO (CRD420251075068).</p>

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Performance of artificial intelligence in automated measurement of patellofemoral joint parameters: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Hongwei Zhan,
  • Zandong Zhao,
  • Qiuzhen Liang,
  • Jiang Zheng,
  • Liang Zhang

摘要

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Systematic review registration

This systematic review was registered in PROSPERO (CRD420251075068).