ArticleTherapeutic advances in musculoskeletal disease2024
Automated osteoporosis classification and
Article in Therapeutic advances in musculoskeletal disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Artificial Intelligence-Assisted Structural Analysis of Bones with Paget's Disease of Bone and Osteoporosis: Lessons from Mouse Models.Diagnostics (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-driven appendicular skeletal muscle mass index (ASMI) prediction and low muscle mass detection from routine hip X-rays: a novel opportunistic case finding tool.European geriatric medicine · 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
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- An AI-based pipeline for osteoporosis/osteopenia prediction using hip radiographs.Skeletal radiology · 2026Article
- Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.Frontiers in artificial intelligence · 2026Article
- Bone Mineral Density, C-Terminal Telopeptide of Type I Collagen, and Osteocalcin as Monitoring Parameters of Bone Remodeling in CML Patients Undergoing Imatinib Therapy: A Basic Science and Clinical Review.Diseases (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%; specificity: 95.6%; positive predictive value: 95.7%; negative predictive value: 97.1%; area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the Conclusion: The proposed CNN model may identify osteoporosis and predict Trial registration: TMU-JIRB N201909036.
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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.