ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2019
Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.
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Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Artificial intelligence in osteoporosis screening and fracture risk prediction among aging populations: a bibliometric evidence map.Frontiers in endocrinology · 2026Pooled it
- [Osteoporosis-Definition, risk assessment, diagnosis, prevention and treatment (update 2024) : Guidelines of the Austrian Society for Bone and Mineral Research].Wiener klinische Wochenschrift · 2024Guideline
- CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Pooled it
- 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
- Osteoporosis: The Renascent Impact of Vertebral Fractures-A Narrative Review of Diagnosis, Risk Stratification, and Integrated Management.Journal of clinical medicine · 2026Review
- Vertebral-level discrimination of incidental vertebral fractures using volumetric BMD, texture features, and finite element-derived fracture load: an exploratory study.Skeletal radiology · 2026Article
- Artificial intelligence on opportunistic computed tomography for predicting vertebral fracture risk in women undergoing estrogen deprivation therapy for breast cancer.La Radiologia medica · 2026Article
- Application of machine learning in osteoporosis screening: a narrative review.NPJ digital medicine · 2026Review
- Radiomics identifies distinct cortical bone texture alterations in patients with CKD using HR-pQCT.Bone research · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Deep Learning-Assisted Radiomics for Predicting Thoracolumbar Osteoporotic Vertebral Fractures.Clinical interventions in aging · 2026Article
- Fine-grained classification of thoracic vertebral compression fractures based on multi-layer feature fusion and attention-guided patch recombination.Biomedical engineering letters · 2025Article
- Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.BMC medical imaging · 2025Article
- Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning-based analysis of CT images.La Radiologia medica · 2025Article
- Opportunistic screening for osteoporosis using artificial intelligence-based morphometric analysis of chest computed tomography images: a retrospective multi-center study in Russia leveraging the COVID-19 pandemic.Asian spine journal · 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
- Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.BMC musculoskeletal disorders · 2025Article
- Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm.BMC veterinary research · 2025Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures.
introductionMany patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures.
methodsIn total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation.
resultsThe random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64).
conclusionThe presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.