ArticleHeliyon2024
Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Deep Learning-Based Texture-Branch Cross-Attention Network.Journal of imaging informatics in medicine · 2026Article
- Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era.Arthroplasty (London, England) · 2026Article
- MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.Scientific reports · 2026Article
- Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.Frontiers in artificial intelligence · 2026Article
- 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
- Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout.Journal of imaging informatics in medicine · 2025Article
- Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection.Biomolecules & biomedicine · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- Radiographic and clinical evaluation of external pedicle screw fixation as a definitive solution for selective acetabular fractures: a retrospective analysis.BMC musculoskeletal disorders · 2024Article
- A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images.Scientific reports · 2024Article
- Impact of metadata in multimodal classification of bone tumours.BMC musculoskeletal disorders · 2024Article
- Radiomics analysis based on plain X-rays to detect spinal fractures with posterior wall injury.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.
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Registered trials
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 Socratic graph.