ArticleJournal of medical Internet research2024
A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Enhanced YOLOv8 with Hybrid Attention Gating Block for Intertrochanteric Fracture Detection in Complex X-Ray Images.Journal of imaging informatics in medicine · 2026Article
- Beyond Bone Density Alone: Opportunistic Identification of Vertebral Compression Fractures in Breast Cancer Survivors Using Artificial Intelligence-Derived Vertebral Bone Density and Paraspinal Muscle-Fat Metrics.Journal of clinical medicine · 2026Article
- Per-Vertebra Prediction of Future Osteoporotic Fractures from Routine Computed Tomography Using a Two-Stage Machine Learning Framework.Medicina (Kaunas, Lithuania) · 2026Article
- Preoperative Computed Tomography Aortic Calcification and Attenuation of the Vertebral Body Predict Fractures Post-Kidney Transplantation Using Machine Learning.Clinical transplantation · 2026Article
- Enhancing vertebral fracture prediction using multitask deep learning computed tomography imaging of bone and muscle.European radiology · 2026Article
- Advances in Imaging-Based Fracture Risk Assessment for Unlocking Latent Skeletal Fragility.Current osteoporosis reports · 2026Review
- Incorporating Artificial Intelligence into Fracture Risk Assessment: Using Clinical Imaging to Predict the Unpredictable.Endocrinology and metabolism (Seoul, Korea) · 2025Review
- Bone Mineral Density and Intermuscular Fat Derived from Computed Tomography Images Using Artificial Intelligence Are Associated with Fracture Healing.Bioengineering (Basel, Switzerland) · 2025Article
- AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences.Scientific reports · 2025Article
- The Role of Computed Tomography and Artificial Intelligence in Evaluating the Comorbidities of Chronic Obstructive Pulmonary Disease: A One-Stop CT Scanning for Lung Cancer Screening.International journal of chronic obstructive pulmonary disease · 2025Review
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- Machine learning approaches to predict hip fracture incidence: insights from the CHARLS dataset.Frontiers in public health · 2025Article
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7 authors.
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Abstract
backgroundWith the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.
objectiveThe aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles.
methodsThe models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles.
resultsThe mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001).
conclusionsThe model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.
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