ArticleScientific reports2024
Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Patellofemoral instability: Anatomy, imaging techniques, and clinically relevant measurements.Skeletal radiology · 2026Review
- Bone Shape Feature Differences in Single and Recurrent Patellar Dislocations via Statistical Shape Modeling.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Article
- MRI-based morphometric analysis of the patellofemoral joint: diagnostic modeling of knee pathologies in adolescents.Frontiers in artificial intelligence · 2026Article
- Interpretable wrapper-based machine learning framework for predicting patellofemoral pain syndrome using minimal clinical tests.Scientific reports · 2025Article
- Artificial Intelligence Has Varied Diagnostic and Predictive Performance in Diagnosing Patellofemoral Osteoarthritis, Trochlear Dysplasia, and Patellofemoral Tracking Abnormalities: A Systematic Review.Arthroscopy, sports medicine, and rehabilitation · 2025Review
- 3D Patellar instability Anatomical Severity Score (3D-PASS): A Novel Machine Learning Score Using 3D Bone Position From MRI to Predict Outcomes of Patellar Instability Treatment (Using a Subset of Data in the JUPITER Cohort).Orthopaedic journal of sports medicine · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Patellofemoral Instability: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Comparative Analysis of 3 Machine Learning Methods for Identifying Minimal Predictors of Patellofemoral Instability Risk Factors.Orthopaedic journal of sports medicine · 2025Article
- Ultrasound-Based Statistical Shape Modeling for Quantifying Femoral Trochlear Bone Shape Post-ACLR.Osteoarthritis imaging · 2025Article
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Authors and funding
9 authors.
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Abstract
This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features.
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