ArticleEuropean radiology2019
Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 2 of them syntheses that pooled it.
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Who cites it
74 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- [Application and prospect of machine learning in orthopaedic trauma].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Pooled it
- Bone Mineral Density of Spine Patients with CT Hounsfield Units and AI Segmentation: A Contemporary Review.Asian journal of neurosurgery · 2026Review
- Per-Vertebra Prediction of Future Osteoporotic Fractures from Routine Computed Tomography Using a Two-Stage Machine Learning Framework.Medicina (Kaunas, Lithuania) · 2026Article
- The Interface of Artificial Intelligence and the Electronic Medical Record in Orthopaedic Surgery: Current Applications and Future Directions.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Using magnetic resonance imaging-based subregional texture analysis models to classify knee osteoarthritis severity by compartment.Scientific reports · 2025Article
- Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Quantitative CT Imaging Radiomics-Based Prediction of Bone Mineral Density Changes After Sleeve Gastrectomy.Obesity surgery · 2025Article
- The effectiveness of Valsalva Maneuver-Assisted percutaneous vertebroplasty in reducing cement leakage in osteoporotic vertebral compression fractures.BMC musculoskeletal disorders · 2025Article
- Conventional chest computed tomography-based radiomics for predicting the risk of thoracolumbar osteoporotic vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- 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
- Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.BMC medical imaging · 2025Article
- Differentiation of Early Sacroiliitis Using Machine-Learning- Supported Texture Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Texture analysis combined with machine learning in radiographs of the knee joint: potential to identify tibial plateau occult fractures.Quantitative imaging in medicine and surgery · 2025Article
- CT-based 3D Super-resolution Radiomics for the Differential Diagnosis of BrucellaCurrent medical imaging · 2025Article
- Radiomics models based on thoracic and upper lumbar spine in chest LDCT to predict low bone mineral density.Scientific reports · 2024Article
- A diagnostic model for differentiating tuberculous spondylodiscitis from pyogenic spondylodiscitis based on pathogen-confirmed patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures. MATERIALS AND
methodsStandard CT scans of 58 patients with insufficiency fractures of the spine, performed between 2006 and 2013, were analyzed retrospectively. Every included patient had at least two CT scans. Intact vertebrae in a first scan that either fractured ("unstable") or remained intact ("stable") in the consecutive scan were manually segmented on mid-sagittal reformations. TA features for all vertebrae were extracted using open-source software (MaZda). In a paired control study, all vertebrae of the study cohort "cases" and matched controls were classified using ROC analysis of Hounsfield unit (HU) measurements and supervised ML techniques. In a within-subject vertebra comparison, vertebrae of the cases were classified into "unstable" and "stable" using identical techniques.
resultsOne hundred twenty vertebrae were included. Classification of cases/controls using ROC analysis of HU measurements showed an AUC of 0.83 (95% confidence interval [CI], 0.77-0.88), and ML-based classification showed an AUC of 0.97 (CI, 0.97-0.98). Classification of unstable/stable vertebrae using ROC analysis showed an AUC of 0.52 (CI, 0.42-0.63), and ML-based classification showed an AUC of 0.64 (CI, 0.61-0.67).
conclusionTA combined with ML allows to identifying patients who will suffer from vertebral insufficiency fractures in standard CT scans with high accuracy. However, identification of single vertebra at risk remains challenging. KEY POINTS: • Bone texture analysis combined with machine learning allows to identify patients at risk for vertebral body insufficiency fractures on standard CT scans with high accuracy. • Compared to mere Hounsfield unit measurements on CT scans, application of bone texture analysis combined with machine learning improve fracture risk prediction. • This analysis has the potential to identify vertebrae at risk for insufficiency fracture and may thus increase diagnostic value of standard CT scans.
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