Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 2, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

7.9K

Frontal plane mechanical leg alignment estimation from knee x-rays using deep learning.

Kenneth Chen1,2, Christoph Stotter3, Christopher Lepenik4

  • 1Department for Health Sciences, Medicine and Research, University of Continuing Education Krems, Krems, Austria.

Osteoarthritis and Cartilage Open
|January 15, 2025
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cell-based therapy injections for the management of knee osteoarthritis: The ESSKA-ICRS consensus. Recommendations using the RAND/UCLA appropriateness method for different clinical scenarios.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2026
Same author

Distinct Extracellular Matrix Protein Signatures of Cortical and Cancellous Bone Allografts Following Processing for Clinical Use.

Cells·2026
Same author

Cyclodextrin-Epichlorohydrin-Cyanoguanidine Polymer for Resveratrol Delivery to Enhance Human Chondrocyte Function in Cartilage Repair.

Biomacromolecules·2026
Same author

Evaluating BCG response in primary and metachronous non-muscle invasive bladder cancer following prior upper tract urothelial cancer: A systematic review and meta-analysis.

Urologic oncology·2026
Same author

A consensus-based classification of minor complications, major complications, and failure after anterior cruciate ligament reconstruction: A modified delphi study.

Journal of ISAKOS : joint disorders & orthopaedic sports medicine·2026
Same author

Evaluating learning curves and robotic skill transfer to hinotori™: CUSUM analysis after complete platform conversion in radical prostatectomy.

World journal of urology·2026
Same journal

Arthroscopically and manually minced cartilage demonstrates lower cell viability and lower proteoglycan deposition compared to isolated chondrons and chondrocytes.

Osteoarthritis and cartilage open·2026
Same journal

Frequency and factors associated with neuropathic pain in patients with knee osteoarthritis.

Osteoarthritis and cartilage open·2026
Same journal

Are magnetic resonance imaging features associated with intermittent and constant pain in knee osteoarthritis? A cross-sectional study.

Osteoarthritis and cartilage open·2026
Same journal

Individuals' perceptions and experiences of mHealth for home-based rehabilitation in knee osteoarthritis: A qualitative study.

Osteoarthritis and cartilage open·2026
Same journal

Recovery of daily-life walking after total knee arthroplasty: A two-year longitudinal study and comparison with healthy controls.

Osteoarthritis and cartilage open·2026
Same journal

Associations of hemoglobin levels with structural knee MRI findings at 33 years of age in a general population-based birth cohort.

Osteoarthritis and cartilage open·2026
See all related articles
This summary is machine-generated.

A new deep learning model accurately identifies lower limb malalignment from knee X-rays. This method aids in selecting study participants and managing patients with knee osteoarthritis (OA).

Area of Science:

  • Orthopedics
  • Radiology
  • Artificial Intelligence

Background:

  • Lower limb malalignment is a significant factor in knee osteoarthritis (OA) progression and symptom severity.
  • Accurate assessment of leg alignment is crucial for patient stratification in clinical studies and treatment planning.

Purpose of the Study:

  • To develop and validate a deep learning model for classifying lower limb alignment from knee antero-posterior (AP)/postero-anterior (PA) radiographs.
  • To assess the model's performance using adjustable hip-knee-ankle (HKA) angle thresholds.

Main Methods:

  • Utilized a dataset of 8878 digital radiographs, including full-leg x-rays (LLRs) and knee x-rays.
  • Employed a two-step validation process comparing model predictions on knee images to ground truth from LLRs.
Keywords:
Artificial intelligenceLeg alignmentOsteoarthritisPredictionRadiograph

More Related Videos

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
08:52

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

23.1K
Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy
08:15

Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy

Published on: February 17, 2023

988

Related Experiment Videos

Last Updated: Jun 2, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

7.9K
3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
08:52

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

23.1K
Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy
08:15

Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy

Published on: February 17, 2023

988

Main Results:

  • The deep learning model demonstrated high accuracy in classifying leg alignment.
  • Sensitivity and specificity ranged from 0.74 to 0.92 across different thresholds and image types (with/without positioning frames).

Conclusions:

  • The developed model accurately classifies lower limb malalignment using only knee radiographs.
  • This approach offers a practical alternative to full leg radiographs (LLRs), improving precision in study selection and patient management.