ArticleScientific reports2024
A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled 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
- 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
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- 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
- Transfer learning based osteoporosis prediction using enhanced medical imaging and fuzzy fusion.Scientific reports · 2025Article
- Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection.Biomolecules & biomedicine · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a new deep-learning approach incorporating a superfluity mechanism to categorize knee X-ray images into osteoporosis, osteopenia, and normal classes. The superfluity mechanism suggests the use of two distinct types of blocks. The rationale is that, unlike a conventional serially stacked layer, the superfluity concept involves concatenating multiple layers, enabling features to flow into two branches rather than a single branch. Two knee datasets have been utilized for training, validating, and testing the proposed model. We use transfer learning with two pre-trained models, AlexNet and ResNet50, comparing the results with those of the proposed model. The results indicate that the performance of the pre-trained models, namely AlexNet and ResNet50, was inferior to that of the proposed Superfluity DL architecture. The Superfluity DL model demonstrated the highest accuracy (85.42% for dataset1 and 79.39% for dataset2) among all the pre-trained models.
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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.