ArticleEuropean radiology2024
Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence in automated measurement of patellofemoral joint parameters: a systematic review.Journal of orthopaedic surgery and research · 2025Pooled it
- 3-D landmarking repeatability emphasizes challenges in scan positioning during weight bearing CT of the knee.Osteoarthritis imaging · 2026Article
- Patellofemoral instability: Anatomy, imaging techniques, and clinically relevant measurements.Skeletal radiology · 2026Review
- Combined trochleoplasty and medial patellofemoral ligament reconstruction reduces patellar height.Journal of experimental orthopaedics · 2026Article
- Automated deep-learning quantification of nine patellofemoral instability parameters on multislice CT images : development and validation of the GU2Net model.Bone & joint open · 2026Article
- Quantifying 3D foot and ankle alignment using an AI-driven framework: a pilot study.Skeletal radiology · 2026Article
- MRI-based morphometric analysis of the patellofemoral joint: diagnostic modeling of knee pathologies in adolescents.Frontiers in artificial intelligence · 2026Article
- Patellar Instability: A Narrative Review with an Integrated Treatment Algorithm.Orthopedic research and reviews · 2026Review
- 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
- Artificial Intelligence for the Diagnosis and Management of Patellofemoral Instability: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Generating 3D pseudo-healthy knee MR images to support trochleoplasty planning.International journal of computer assisted radiology and surgery · 2025Article
- Recurrent patellar dislocation: treatments and challenges.Frontiers in surgery · 2025Review
- Patellofemoral Pain Syndrome: Focused Vibrations Plus Kinesiotaping with Insights into Radiological Influences-An Observational Study.Journal of functional morphology and kinesiology · 2024Article
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Authors and funding
5 authors.
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Abstract
objectivesTo develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.
methodsA total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks' locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices.
resultsThe developed models achieved good accuracy in predicting the landmarks' locations, with a maximum value for the MAE of 1.38 ± 0.76 mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC > 0.9), and SA, TFA and MISI can be measured with good reliability (ICC > 0.75), with the proposed framework.
conclusionsThis study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice. CLINICAL RELEVANCE STATEMENT: The objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment. KEY POINTS: • Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability. • Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI). • The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists.
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