ArticleRadiology. Artificial intelligence2021
Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial Intelligence.
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37 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
- An artificial intelligence framework for universal landmark matching and morphometry in musculoskeletal radiography.European radiology · 2026Article
- Radiographic Analysis of Lower Limb Alignment in Patients with Knee Osteoarthritis.Tomography (Ann Arbor, Mich.) · 2026Article
- Automated assessment of coronal lower extremity alignment on long-leg radiographs using a deep-learning model: validation, efficiency gains, and superiority of an edge-based tibial joint-line definition.BMC musculoskeletal disorders · 2026Article
- Combining deep learning with statistical shape modelling enables automated lower limb measurements with observer-level reliability using weight-bearing computed tomography.Journal of experimental orthopaedics · 2026Article
- [Artificial intelligence in musculoskeletal radiology : Current advances and emerging frontiers].Orthopadie (Heidelberg, Germany) · 2026Article
- Indications, Technique, and Outcomes of Patient Specific Instrumentation for Osteotomy About the Knee.Current reviews in musculoskeletal medicine · 2025Review
- 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
- [Current Landscape and Commercialization of AI Models in Musculoskeletal Imaging].Journal of the Korean Society of Radiology · 2025Review
- Automated radiographic assessment of lower limb alignment using deep learning in a data-constrained clinical setting.BMC musculoskeletal disorders · 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
- Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Analysis of Genu Varum in Young Male Professional Soccer Players.Orthopaedic journal of sports medicine · 2025Article
- Frontal plane mechanical leg alignment estimation from knee x-rays using deep learning.Osteoarthritis and cartilage open · 2025Article
- Comparison of an AI-driven planning tool and manual radiographic measurements in total knee arthroplasty.Computational and structural biotechnology journal · 2025Article
- Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis.The archives of bone and joint surgery · 2025Review
- Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.Knee surgery & related research · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Automated analysis of knee joint alignment using detailed angular values in long leg radiographs based on deep learning.Scientific reports · 2024Article
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Authors and funding
9 authors.
Justus SchockDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0003-0512-3053
Daniel TruhnDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0002-9605-0728
Daniel B AbrarDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).
Dorit MerhofDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0002-1672-2185
Stefan ConradDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0003-2788-3854
Manuel PostDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0003-2810-6432
Felix MittelstrassDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).
Christiane KuhlDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0001-8696-2363
Sven NebelungDepartment of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).ORCID 0000-0002-5267-9962
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a deep learning-based method for automatic quantitative analysis of lower-extremity alignment. MATERIALS AND
methodsIn this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data (
resultsBilateral LLRs of 255 patients (mean age, 26 years ± 23 [standard deviation]; range, 0-88 years; 157 male patients) were included. Mean Sørensen-Dice coefficients for segmentation were 0.97 ± 0.09 for the femur and 0.96 ± 0.11 for the tibia. Mean HKAAs and AMAs as measured by the readers and the algorithm ranged from 0.05° to 0.11° (
conclusionFully automated analysis of LLRs yielded accurate results across a wide range of clinical and pathologic indications and is fast enough to enhance and accelerate clinical workflows.
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