SynthesisJournal of orthopaedic surgery and research2025
Performance of artificial intelligence in automated measurement of patellofemoral joint parameters: a systematic review.
Synthesis in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Patellofemoral instability: Anatomy, imaging techniques, and clinically relevant measurements.Skeletal radiology · 2026Review
- In Vivo Classification of Patellar Motion Trajectories in Individuals: A 4D-CT-Based Study with Unsupervised Clustering.Diagnostics (Basel, Switzerland) · 2026Article
- The Lateral Alignment Angle: A Novel Method for Evaluating Lateral Patellar Tilt With High Accuracy, Good Reproducibility, and Independence From Patellofemoral Joint Morphology.Orthopaedic surgery · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsA 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.
resultsA 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.
conclusionAI-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).
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