ArticleNature communications2021
Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 3 of them syntheses that pooled it.
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Who cites it
84 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2024Pooled it
- Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.BMJ open · 2023Pooled it
- Validity of Bone Mineral Density Measurements Using SONIALVISION G4: A Quantitative CT-based Evaluation.Journal of bone and mineral metabolism · 2026Article
- IgeaNet: a deep learning model for opportunistic osteoporosis screening from chest X-ray images.La Radiologia medica · 2026Article
- Radiographic Classification of Low-Energy Distal Radius Fracture Status Using Automated Hand Segmentation.Journal of imaging informatics in medicine · 2026Article
- AI for Opportunistic Identification of BMD and Bone Microarchitecture: A Narrative Review.Calcified tissue international · 2026Review
- Hip anteroposterior radiographic indices for effective osteoporosis screening in female patients undergoing total hip arthroplasty.Archives of osteoporosis · 2026Article
- Opportunistic bone mineral density assessment in pediatric scoliosis: validating Hounsfield unit measurement from lumbar CT images.Spine deformity · 2026Article
- Knee-specific bone quality assessment for cementless total knee arthroplasty candidate selection: a narrative review.Knee surgery & related research · 2026Review
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.BMC musculoskeletal disorders · 2026Article
- Clinical impact of adopting an AI-driven opportunistic bone health screening software using routine X-ray.Skeletal radiology · 2026Article
- Standardizing attenuation across tube voltages and vertebral levels for opportunistic osteoporosis screening on low-dose chest CT.Radiology advances · 2026Article
- Artificial intelligence-assisted screening reveals high prevalence of osteoporosis in Vietnamese adults using pelvic and hip radiographs.Osteoporosis and sarcopenia · 2026Article
- An AI-based pipeline for osteoporosis/osteopenia prediction using hip radiographs.Skeletal radiology · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Comprehensive opportunistic osteoporosis screening with AI: retrospective development and prospective validation of dual-site BMD prediction from a single radiograph.Frontiers in endocrinology · 2026Article
- Molecular mechanisms and therapeutic implications of the sympathetic nervous system in bone-related disorders: a brain-bone axis perspective.Bone research · 2025Review
24 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
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Abstract
Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = -0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.
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