ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2023
Deep learning-based landmark recognition and angle measurement of full-leg plain radiographs can be adopted to assess lower extremity alignment.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based assessment of leg axis parameters shows excellent agreement with human raters: A systematic review and meta-analysis.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Pooled it
- Precision of AI-Based Lower Limb Assessment in Children and Adolescents with Open Growth Plates.Diagnostics (Basel, Switzerland) · 2026Article
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Article
- Dynamic changes to the tibiofemoral joint line with increasing osteoarthritis severity and its relationship to constitutional alignment : a radiological analysis of 3,320 knees.Bone & joint open · 2026Article
- Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.The archives of bone and joint surgery · 2026Article
- High agreement of three-dimensional lower limb alignment analysis between a novel cone-beam CT and multislice CT.Journal of experimental orthopaedics · 2025Article
- Are artificial intelligence generated lower extremity radiographic measurements accurate in a cohort with implants?Skeletal radiology · 2025Article
- A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.Journal of orthopaedic surgery and research · 2025Article
- Automated radiographic assessment of lower limb alignment using deep learning in a data-constrained clinical setting.BMC musculoskeletal disorders · 2025Article
- Artificial intelligence-guided assessment of the hip-knee-ankle angle shows excellent correlation with experienced human raters.Journal of experimental orthopaedics · 2025Article
- An AI-based system for fully automated knee alignment assessment in standard AP knee radiographs.The Knee · 2025Article
- High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Automatic assessment of lower limb deformities using high-resolution X-ray images.BMC musculoskeletal disorders · 2025Article
- Planning of distal femoral osteotomy - antegrade vs. retrograde miniaci method.Archives of orthopaedic and trauma surgery · 2025Article
- Do knee alignment patterns differ between Middle Eastern and East Asian populations? A propensity-matched analysis using artificial intelligence.Knee surgery & related research · 2025Article
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis.The archives of bone and joint surgery · 2025Review
- Augmentation with Bone Marrow Aspirate Harvested from the Iliac Crest for Horizontal or Radial Meniscal Tears Yields Favorable Healing Rates in Magnetic Resonance Imaging and Clinical Outcomes.Clinics in orthopedic surgery · 2024Article
- A deep learning based automatic two-dimensional digital templating model for total knee arthroplasty.Knee surgery & related research · 2024Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeEvaluating lower extremity alignment using full-leg plain radiographs is an essential step in diagnosis and treatment of patients with knee osteoarthritis. The study objective was to present a deep learning-based anatomical landmark recognition and angle measurement model, using full-leg radiographs, and validate its performance.
methodsA total of 11,212 full-leg plain radiographs were used to create the model. To train the data, 15 anatomical landmarks were marked by two orthopaedic surgeons. Mechanical lateral distal femoral angle (mLDFA), medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), and hip-knee-ankle angle (HKAA) were then measured. For inter-observer reliability, the inter-observer intraclass correlation coefficient (ICC) was evaluated by comparing measurements from the model, surgeons, and students, to ground truth measurements annotated by an orthopaedic specialist with 14 years of experience. To evaluate test-retest reliability, all measurements were made twice by each measurer. Intra-observer ICCs were then derived. Performance evaluation metrics used in previous studies were also derived for direct comparison of the model's performance.
resultsInter-observer ICCs for all angles of the model were 0.98 or higher (p < 0.001). Intra-observer ICCs for all angles were 1.00, which was higher than that of the orthopaedic specialist (0.97-1.00). Measurements made by the model showed no significant systemic variation. Except for JLCA, angles were precisely measured with absolute error averages under 0.52 degrees and proportion of outliers under 4.26%.
conclusionsThe deep learning model is capable of evaluating lower extremity alignment with performance as accurate as an orthopaedic specialist with 14 years of experience. LEVEL OF EVIDENCE: III, retrospective cohort study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.