ArticleBMC musculoskeletal disorders2023
The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures.
Article in BMC musculoskeletal disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- Attention-Guided EfficientNet-B3 with Grad-CAM Visualization for 22-Class Bone Fracture and Anatomical-Region Classification on the MultiBoneX Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Performance Comparison of Machine Learning Using Radiomic Features and CNN-Based Deep Learning in Benign and Malignant Classification of Vertebral Compression Fractures Using CT Scans.Journal of imaging informatics in medicine · 2026Article
- Local fragility index and Radiomics-lite modeling of CT-Derived Hounsfield units for osteoporosis risk assessment in spine surgery 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 · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Deep Learning-Assisted Radiomics for Predicting Thoracolumbar Osteoporotic Vertebral Fractures.Clinical interventions in aging · 2026Article
- Molecular Mechanisms and Therapeutic Targets for Pain Following Osteoporotic Vertebral Fractures.Journal of pain research · 2026Review
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- From Detection to Decision: T-Score-Guided Use of STIR MRI After CT in Vertebral Fractures.Diagnostics (Basel, Switzerland) · 2025Article
- Temporal trends in the burden of vertebral fractures caused by falls in China and globally from 1990 to 2021: a systematic analysis of the Global Burden of Disease Study 2021.Archives of public health = Archives belges de sante publique · 2025Article
- Comparison of Artificial Intelligence Models Using CT Radiomics for Predicting Post-Vertebral Augmentation Residual Back Pain in Osteoporotic Vertebral Compression Fractures.International journal of medical sciences · 2025Article
- Integrating radiomics with clinical data for enhanced prediction of vertebral fracture risk.Frontiers in bioengineering and biotechnology · 2024Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). MATERIALS AND
methods128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model.
resultsFor mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846.
conclusionMachine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.
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Registered trials
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.