ArticleEuropean radiology2023
Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Accuracy of Low-Dose Chest CT-Based Artificial Intelligence Models in Osteoporosis Detection: A Systematic Review and Meta-analysis.Calcified tissue international · 2025Pooled it
- Diagnostic performance of radiomics for predicting osteoporosis in adults: 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
- Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.Journal of bone and mineral metabolism · 2024Pooled it
- AI-enhanced routine CT for opportunistic osteoporosis screening and risk assessment.Skeletal radiology · 2026Review
- An open-source CT texture phantom for radiomic imaging studies: Design, manufacture, and initial evaluation.Medical physics · 2026Article
- Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.Abdominal radiology (New York) · 2026Article
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- Article
- Deep Learning-Based Assessment of the Value of Vertebral Structural Parameters in Predicting Osteoporotic Vertebral Compression Fractures on Opportunistic CT Scans.International journal of general medicine · 2026Article
- Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture.PLoS computational biology · 2025Article
- Artificial intelligence system for predicting areal bone mineral density from plain X-rays.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Fusion of X-Ray Images and Clinical Data for a Multimodal Deep Learning Prediction Model of Osteoporosis: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Article
- 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
- The application progress of artificial intelligence in osteoporosis diagnosis.Frontiers in artificial intelligence · 2025Review
- Application of machine learning on health examination data for predicting the decrease of bone mineral density.International journal of clinical and experimental pathology · 2025Article
- 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
- Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT).
methodsA total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation.
resultsThe model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05.
conclusionThe combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening. KEY POINTS: • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.
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