ArticleScientific reports2023
A deep learning approach for fully automated measurements of lower extremity alignment in radiographic images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- 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
- Deep Learning-Based Full-Process Automatic CPAK Classification System and Its Application in the Analysis of Alignment Outcomes Before and After Knee Arthroplasty.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing automatic landmark localization in X-ray images using combined segmentation and regression models: application to lower limb alignment assessment.Scientific reports · 2026Article
- A Hybrid Automatic Model for Circle Detection in X-Ray Imagery: A Case Study on Hip Prosthesis Wear.Bioengineering (Basel, Switzerland) · 2026Article
- [Current Landscape and Commercialization of AI Models in Musculoskeletal Imaging].Journal of the Korean Society of Radiology · 2025Review
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Automated radiographic assessment of lower limb alignment using deep learning in a data-constrained clinical setting.BMC musculoskeletal disorders · 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
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages.BMC musculoskeletal disorders · 2024Article
- Enhanced deep learning model enables accurate alignment measurement across diverse institutional imaging protocols.Knee surgery & related research · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During clinical evaluation of patients and planning orthopedic treatments, the periodic assessment of lower limb alignment is critical. Currently, physicians use physical tools and radiographs to directly observe limb alignment. However, this process is manual, time consuming, and prone to human error. To this end, a deep-learning (DL)-based system was developed to automatically, rapidly, and accurately detect lower limb alignment by using anteroposterior standing X-ray medical imaging data of lower limbs. For this study, leg radiographs of non-overlapping 770 patients were collected from January 2016 to August 2020. To precisely detect necessary landmarks, a DL model was implemented stepwise. A radiologist compared the final calculated measurements with the observations in terms of the concordance correlation coefficient (CCC), Pearson correlation coefficient (PCC), and intraclass correlation coefficient (ICC). Based on the results and 250 frontal lower limb radiographs obtained from 250 patients, the system measurements for 16 indicators revealed superior reliability (CCC, PCC, and ICC ≤ 0.9; mean absolute error, mean square error, and root mean square error ≥ 0.9) for clinical observations. Furthermore, the average measurement speed was approximately 12 s. In conclusion, the analysis of anteroposterior standing X-ray medical imaging data by the DL-based lower limb alignment diagnostic support system produces measurement results similar to those obtained by radiologists.
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