ArticleEndocrinology and metabolism (Seoul, Korea)2022
Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm.
Article in Endocrinology and metabolism (Seoul, Korea), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- Enhancing vertebral fracture prediction using multitask deep learning computed tomography imaging of bone and muscle.European radiology · 2026Article
- Validation of a deep learning model for bone fragility detection from conventional radiographs: an international cohort study.EClinicalMedicine · 2026Article
- Advances in Imaging-Based Fracture Risk Assessment for Unlocking Latent Skeletal Fragility.Current osteoporosis reports · 2026Review
- Hotspots and Trends in the Application of Artificial Intelligence in Spine Medicine from 2005 to 2024: A Bibliometric and Visualization Analysis.Indian journal of orthopaedics · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Deep learning model for differentiating acute and chronic osteoporotic vertebral compressive fractures on multidetector CT: a retrospective study with external validation.Frontiers in medicine · 2026Article
- Machine learning models for predicting survival in patients of hepatocellular carcinoma with second primary malignancy.Translational cancer research · 2025Article
- Incorporating Artificial Intelligence into Fracture Risk Assessment: Using Clinical Imaging to Predict the Unpredictable.Endocrinology and metabolism (Seoul, Korea) · 2025Review
- Accuracy of artificial intelligence in prediction of osteoporotic fractures in comparison with dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool: A systematic review.World journal of orthopedics · 2025Article
- Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.Scientific reports · 2025Article
- Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation.Frontiers in medicine · 2025Review
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.Orthopaedic surgery · 2024Article
- A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.Journal of medical Internet research · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Deep learning in the radiologic diagnosis of osteoporosis: a literature review.The Journal of international medical research · 2024Review
- Patient-specific finite element modeling for fracture risk prediction in a canine model of osteosarcoma.Annals of translational medicine · 2024Article
- Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.Journal of clinical medicine · 2024Review
- AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.Journal of orthopaedic surgery and research · 2023Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgruoundSince image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.
methodsThis study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models.
resultsOf the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women.
conclusionDeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
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