ArticleBiomedicines2022
Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- 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
- Bone Mineral Density of Spine Patients with CT Hounsfield Units and AI Segmentation: A Contemporary Review.Asian journal of neurosurgery · 2026Review
- Diagnostic accuracy of an artificial intelligence-based osteoporosis screening system on portable chest radiographs.Journal of thoracic disease · 2026Article
- Validation of a deep learning model for bone fragility detection from conventional radiographs: an international cohort study.EClinicalMedicine · 2026Article
- Application of machine learning in osteoporosis screening: a narrative review.NPJ digital medicine · 2026Review
- 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
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.NPJ digital medicine · 2025Review
- An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation.Journal of applied clinical medical physics · 2025Article
- Cost-effectiveness of opportunistic osteoporosis screening using artificial-intelligence assisted chest radiographs in Japan.Osteoporosis and sarcopenia · 2025Article
- Performance evaluation of deep learning-based osteoporosis diagnostic models with conventional chest X-ray in a clinical cohort.Journal of thoracic disease · 2025Article
- Unveiling risk factors and predicting osteoporosis through bone density based aging model: a community-based cohort in Guangdong, China.BMC musculoskeletal disorders · 2025Article
- Opportunistic screening of low bone mass using knowledge distillation-based deep learning in chest X-rays with external validations.Archives of osteoporosis · 2025Article
- Validation of a Self-Assessment Tool for Osteoporosis and Vertebral Fracture Primary Screening in Elderly Men.Journal of evidence-based medicine · 2025Article
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7 authors.
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Abstract
Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.
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