ArticleJournal of bone and mineral metabolism2023
A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis.
Article in Journal of bone and mineral metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis.Skeletal radiology · 2026Article
- A comprehensive evaluation of seven osteoporosis screening tools.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Review
- Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- CT-based machine learning radiomics modeling to screen for lumbar spine osteoporosis.Frontiers in medicine · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Machine learning is changing osteoporosis detection: an integrative review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.Biomedical engineering online · 2025Article
- Comparison of Resampling Methods and Radiomic Machine Learning Classifiers for Predicting Bone Quality Using Dual-Energy X-Ray Absorptiometry.Diagnostics (Basel, Switzerland) · 2025Article
- Integration of MRI radiomics features and clinical data for predicting neurological recovery after thoracic spinal stenosis surgery: a machine learning model.Frontiers in medicine · 2025Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- A single sequence MRI-based deep learning radiomics model in the diagnosis of early osteonecrosis of femoral head.Frontiers in bioengineering and biotechnology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionThe aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications. MATERIALS AND
methodsA retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA).
resultsA total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort.
conclusionThe combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.
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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.