ReviewBioengineering (Basel, Switzerland)2023
Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- 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
- The neuro-skeletal crosstalk: Mechanisms, clinical implications, and smart material interventions.Journal of orthopaedic translation · 2026Review
- Application of machine learning in osteoporosis screening: a narrative review.NPJ digital medicine · 2026Review
- Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.The Indian journal of radiology & imaging · 2026Review
- Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.NPJ digital medicine · 2026Article
- Article
- Evaluating an Artificial Intelligence software for opportunistic low bone mineral density and osteoporosis screening: a validation study.JBMR plus · 2026Article
- Enhancing osteoporosis treatment: emerging roles of engineered exosomes in bone regeneration and repair.Journal of translational medicine · 2026Review
- Intelligent Attention-Driven Deep Learning for Hip Disease Diagnosis: Fusing Multimodal Imaging and Clinical Text for Enhanced Precision and Early Detection.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Osteoporosis: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
- Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025Review
- Artificial Intelligence in Postmenopausal Health: From Risk Prediction to Holistic Care.Journal of clinical medicine · 2025Review
- Integration of Multi-Scale Predictive Tools of Bone Fragility: A Structural and Material Property Perspective.Materials (Basel, Switzerland) · 2025Review
- Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes.Head & face medicine · 2025Article
- AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences.Scientific reports · 2025Article
- Relationship of Chronic Stress and Hypertension with Bone Resorption.Journal of functional morphology and kinesiology · 2025Review
- Transferable CNN-based data mining approaches for medical imaging: application to spine DXA scans for osteoporosis detection.Frontiers in computational neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
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What OpenQuestion holds
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.