ReviewDiagnostics (Basel, Switzerland)2024
Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 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
- Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.Frontiers in medicine · 2025Pooled it
- Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2024Pooled it
- Can artificial intelligence-derived bone mineral density outperform traditional morphometric measurements in opportunistic osteoporosis screening using pelvic radiographs? A multicenter comparative diagnostic study.Osteoporosis and sarcopenia · 2026Article
- Diagnostic accuracy of an artificial intelligence-based osteoporosis screening system on portable chest radiographs.Journal of thoracic disease · 2026Article
- AI-derived bone mineral density from standard radiographs compared with DXA for fracture prediction in a 10-year real-world cohort study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Artificial intelligence enabled prediction of hip fragility fracture risk using hip radiograph: an attractive approach yet more work needs to be done.Skeletal radiology · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Deep learning in detection of osteoporosis: insights from recent evidence, and future directions.Annals of medicine and surgery (2012) · 2026Article
- Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.PloS one · 2026Article
- Impact of high body fat and low muscle mass on bone mineral density in postmenopausal women with type 2 diabetes: A DXA-based cross-sectional study.Aging clinical and experimental research · 2025Article
- Opportunistic assessment of osteoporosis using hip and pelvic X-rays with OsteoSight™: validation of an AI-based tool in a US population.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Transferable CNN-based data mining approaches for medical imaging: application to spine DXA scans for osteoporosis detection.Frontiers in computational neuroscience · 2025Article
- AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.PloS one · 2025Article
- Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Article
- Towards the diagnosis of osteoporosis - contributions from coincidental diagnostic imaging findings in chiropractors' practice.Chiropractic & manual therapies · 2024Review
- Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.
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