SynthesisBMC musculoskeletal disorders2024
Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.
Synthesis in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Low-Dose Chest CT-Based Artificial Intelligence Models in Osteoporosis Detection: A Systematic Review and Meta-analysis.Calcified tissue international · 2025Pooled it
- Artifact-Controlled Multi-Vertebral Transfer Learning Ensemble for Patient-Level Three-Class Osteoporosis Screening Using Thoracoabdominal CT-Derived Lumbar Images.Biomedicines · 2026Article
- Early Identification of Low Bone Density Risk Using a Radiofrequency Echographic Multi Spectrometry-Based Prediction Model.Life (Basel, Switzerland) · 2026Article
- An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.BMC musculoskeletal disorders · 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
- Interpretable neural network for risk stratification and drug target discovery based on PBMC transcriptomes.iScience · 2026Article
- Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.NPJ digital medicine · 2026Article
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.Current osteoporosis reports · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Predicting Hip Osteoporosis with Routine Demographic and Biochemical Data: The Shao HipOsteoRisk Model.Clinical interventions in aging · 2026Observational
- Sequence-specific radiomics for diagnosis of spinal bone loss.Frontiers in endocrinology · 2026Article
- Systemic Inflammation in Hip Fracture and Osteoarthritis: Insights into Pathways of Immunoporosis.International journal of molecular sciences · 2025Article
- Deep Learning Classification of Rheumatoid Arthritis in Hand Radiographs Interpretability Insights and Web Application.ImmunoTargets and therapy · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOsteoporosis is one of the most common metabolic diseases that is characterized by a decrease in bone density and a loss of the quality of the bone structure. The use of deep learning in the prediction of osteoporosis can provide a non-invasive, cost-effective, and efficient approach. The aim of this study is to investigate the diagnostic accuracy of deep learning in the prediction of osteoporosis.
methodsThis is a systematic review and meta-analysis study that was conducted on the diagnostic accuracy of deep learning algorithms for predicting osteoporosis. A literature search was performed in electronic databases including PubMed, Elsevier, and Google Scholar to identify relevant articles until December 1, 2023. Articles were searched in databases by combining related terms such as "deep learning", "convolutional neural network", and "osteoporosis". We conducted title, abstract, and full-text screening based on inclusion/exclusion criteria. Various metrics, such as sensitivity, specificity, and area under the curve (AUC), were used to assess the diagnostic performance of deep learning models.
resultsOut of the 181 articles initially identified, 10 studies were included in the analysis. All studies used a convolutional neural network (CNN) as the deep learning model. Three studies investigated multiple deep learning models. Eight studies used various architectures of CNN, such as ResNet, VGG, and EfficientNet. The pooled sensitivity and specificity were 0.86 (95% CI, 0.82-0.89) and 0.89 (95% CI, 0.85-0.91), respectively. The bivariate approach's pooled SROC curve produced an AUC of 0.94 (95% CI 0.91-0.95). The Diagnostic Odds Ratio (DOR) for the deep learning models was 49.09 (95% CI, 28.74-83.84). Deeks' funnel plot asymmetry test (P = 0.4) suggested no potential publication bias.
conclusionsDeep learning has an acceptable performance for the diagnosis of osteoporosis, even better than other ML algorithms. However, further research is needed to validate the findings of this study in clinical trials.
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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.