ArticleMultimedia tools and applications2023
Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network.
Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Deep Learning-Based Texture-Branch Cross-Attention Network.Journal of imaging informatics in medicine · 2026Article
- MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.Scientific reports · 2026Article
- Accurate classification and prediction of knee osteoarthritis based on Al-Biruni Earth Radius metaheuristic optimizer and LSTM classifier.Scientific reports · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- An AI-based pipeline for osteoporosis/osteopenia prediction using hip radiographs.Skeletal radiology · 2026Article
- Article
- Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach.Scientific reports · 2025Article
- Fusion of X-Ray Images and Clinical Data for a Multimodal Deep Learning Prediction Model of Osteoporosis: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Angiogenesis in rheumatoid Arthritis: Pathological characterization, pathogenic mechanisms, and nano-targeted therapeutic strategies.Bioactive materials · 2025Review
- Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.Scientific reports · 2025Article
- The application progress of artificial intelligence in osteoporosis diagnosis.Frontiers in artificial intelligence · 2025Review
- Deep learning meets chest X-rays: a promising approach for predicting future compression fracture risk.Therapeutic advances in musculoskeletal disease · 2025Article
- A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images.Scientific reports · 2024Article
- A few-shot learning framework for the diagnosis of osteopenia and osteoporosis using knee X-ray images.The Journal of international medical research · 2024Article
- Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images.Scientific reports · 2024Article
- Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning in the radiologic diagnosis of osteoporosis: a literature review.The Journal of international medical research · 2024Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis degrades the quality of bones and is the primary cause of fractures in the elderly and women after menopause. The high diagnostic and treatment costs urge the researchers to find a cost-effective diagnostic system to diagnose osteoporosis in the early stages. X-ray imaging is the cheapest and most common imaging technique to detect bone pathologies butmanual interpretation of x-rays for osteoporosis is difficult and extraction of required features and selection of high-performance classifiers is a very challenging task. Deep learning systems have gained the popularity in image analysis field over the last few decades. This paper proposes a convolution neural network (CNN) based approach to detect osteoporosis from x-rays. In our study, we have used the transfer learning of deep learning-based CNNs namely AlexNet, VggNet-16, ResNet, and VggNet -19 to classify the x-ray images of knee joints into normal, osteopenia, and osteoporosis disease groups. The main objectives of the current study are: (i) to present a dataset of 381 knee x-rays medically validated by the T-scores obtained from the Quantitative Ultrasound System, and (ii) to propose a deep learning approach using transfer learning to classify different stages of the disease. The performance of these classifiers is compared and the best accuracy of 91.1% is achieved by pretrained Alexnet architecture on the presented dataset with an error rate of 0.09 and validation loss of 0.54 as compared to the accuracy of 79%, an error rate of 0.21, and validation loss of 0.544 when pretrained network was not used.. The results of the study suggest that a deep learning system with transfer learning can help clinicians to detect osteoporosis in its early stages hence reducing the risk of fractures.
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