ArticleLa Radiologia medica2022
Automatic detection and classification of knee osteoarthritis using deep learning approach.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis.European radiology · 2025Pooled it
- Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.Advanced healthcare materials · 2026Review
- MRI-Based Deep Learning Image Classification for Screening Temporomandibular Joint Degenerative Joint Disease.Journal of clinical medicine · 2026Article
- Moderate-to-substantial agreement of ChatGPT-5 for Kellgren-Lawrence grading on synthetic knee radiographs: a controlled cross-sectional observer agreement study.Rheumatology international · 2026Article
- Evolving Strategies for Knee Osteoarthritis: A Narrative Review of Integrated Rehabilitation, Pharmacologic, and Joint-Preserving Interventions.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Novel algorithm for knee localization and diagnosis and grading of knee osteoarthritis based on a priori information: data from OAI.BMC medical imaging · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Automated Kellgren-Lawrence grading of knee osteoarthritis using a multi-scale attention-based deep learning framework.Frontiers in medicine · 2026Article
- Noise-robust markerless video gait anomaly detection via two-stage acquisition and LSTM autoencoders.Scientific reports · 2025Article
- Deep learning for appendicitis: development of a three-dimensional localization model on CT.Japanese journal of radiology · 2025Article
- TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Knee osteoarthritis rehabilitation: an integrated framework of exercise, nutrition, biomechanics, and physical therapist guidance-a narrative review.European journal of medical research · 2025Review
- Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images.Scientific reports · 2025Article
- Kellgren-Lawrence grading of knee osteoarthritis using deep learning: Diagnostic performance with external dataset and comparison with four readers.Osteoarthritis and cartilage open · 2025Article
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Article
- Potential of AI-based diagnostic grading system for knee osteoarthritis.Frontiers in medicine · 2025Article
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning.Scientific reports · 2024Article
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2 authors.
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Abstract
purposeWe developed a tool for locating and grading knee osteoarthritis (OA) from digital X-ray images and illustrate the possibility of deep learning techniques to predict knee OA as per the Kellgren-Lawrence (KL) grading system. The purpose of the project is to see how effectively an artificial intelligence (AI)-based deep learning approach can locate and diagnose the severity of knee OA in digital X-ray images.
methodsSelection criteria: Patients above 50 years old with OA symptoms (knee joint pain, stiffness, crepitus, and functional limitations) were included in the study. Medical experts excluded patients with post-surgical evaluation, trauma, and infection from the study. We used 3172 Anterior-posterior view knee joint digital X-ray images. We have trained the Faster RCNN architecture to locate the knee joint space width (JSW) region in digital X-ray images and we incorporate ResNet-50 with transfer learning to extract the features. We have used another pre-trained network (AlexNet with transfer learning) for the classification of knee OA severity. We trained the region proposal network (RPN) using manual extract knee area as the ground truth image and the medical experts graded the knee joint digital X-ray images based on the Kellgren-Lawrence score. An X-ray image is an input for the final model, and the output is a Kellgren-Lawrence grading value.
resultsThe proposed model identified the minimal knee JSW area with a maximum accuracy of 98.516%, and the overall knee OA severity classification accuracy was 98.90%.
conclusionsToday numerous diagnostic methods are available, but tools are not transparent and automated analysis of OA remains a problem. The performance of the proposed model increases while fine-tuning the network and it is higher than the existing works. We will extend this work to grade OA in MRI data in the future.
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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.