ArticleScientific reports2023
Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray 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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model.Journal of Korean medical science · 2026Article
- Clinically integrated multi-modal transformer framework with cross-modal gated fusion and clinical nomogram for automated Kellgren-Lawrence grading of knee osteoarthritis on x-ray images.BMC musculoskeletal disorders · 2026Article
- Knee Osteoarthritis Severity Grading Using Contrastive Learning Image Pre-Training.Journal of personalized medicine · 2026Article
- Artificial intelligence in orthopedics: current applications, challenges, and future directions.Knee surgery & related research · 2026Review
- Multimodal model integrating ultrasound and demographic data for the diagnosis of knee osteoarthritis.BMC medical imaging · 2026Article
- Cross-Institutional Five-Class Kellgren-Lawrence Grading of Knee Osteoarthritis via Multitask Deep Learning.Annals of the New York Academy of Sciences · 2026Article
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- Hybrid Ensemble Model for Knee Osteoarthritis Grading: Integrating CNNs with GLCM Features and XAI.Diagnostics (Basel, Switzerland) · 2026Article
- Reliability of radiographic knee and hip osteoarthritis classification between radiologists and orthopaedic surgeons.Journal of orthopaedic surgery and research · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in Kellgren-Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision.Therapeutic advances in musculoskeletal disease · 2026Review
- Recent Advances in Therapeutic Approaches for Knee Osteoarthritis: a Narrative Review.Biomolecules & therapeutics · 2026Review
- An explainable ResNet50-BiLSTM-attention framework with spatial token modeling and imbalance-aware learning for multi-class knee osteoarthritis severity grading.Frontiers in medicine · 2026Article
- Deep learning-based diagnosis of temporomandibular joint osteoarthritis using whole-body bone scans.iScience · 2025Article
- Interpretable multi-model deep learning framework for automated four-class diagnosis of ocular toxoplasmosis using fundus imaging.Scientific reports · 2025Article
- Development and validation of a deep learning model for automatic severity grading of hip osteoarthritis: a multi-center study.Annals of medicine · 2025Article
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- A novel classifier of radiographic knee osteoarthritis for use on knee DXA images is predictive of joint replacement in UK Biobank.Rheumatology advances in practice · 2025Article
- A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays.Biomedical engineering letters · 2025Review
- CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis.Journal of pathology informatics · 2024Article
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4 authors.
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Abstract
The Kellgren-Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician's subjective assessment. Moreover, the accuracy varies significantly depending on the clinician's experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis.
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