SynthesisBiomedical engineering online2023
Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.
Synthesis in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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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
16 citing papers in PubMed, 4 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2024Pooled it
- Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.Journal of bone and mineral metabolism · 2024Pooled it
- A Clinical Primer on Computer Vision.Calcified tissue international · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region.Scientific reports · 2025Article
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- Exploring the Constituents and Mechanisms ofCombinatorial chemistry & high throughput screening · 2025Article
- Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.Current medicinal chemistry · 2025Review
- Emerging Role of Enzyme-Immobilized Nanocarriers in Osteoporosis: Advances and Challenges.International journal of nanomedicine · 2025Review
- Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS).Endocrinology, diabetes & metabolism · 2025Article
- Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- [Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].Problemy endokrinologii · 2024Article
- Review
- Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.Current medicinal chemistry · 2024Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOsteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images.
methodsThe ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis.
resultsThe pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I
conclusionOsteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN).
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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 OpenQuestion graph.