ArticleDiagnostic and interventional radiology (Ankara, Turkey)2024
Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Deep Learning-Assisted Automated Diagnosis of Osteoporosis Based on Computed Tomography Scans: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2024Pooled it
- Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects.World journal of methodology · 2026Review
- Comparison of dual-energy X-ray absorptiometry and lumbar radiographs using deep learning for osteoporosis diagnosis.Journal of bone and mineral metabolism · 2026Article
- Automated classification of musculoskeletal abnormalities using a dynamic ensemble of deep vision models.Medical & biological engineering & computing · 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
- An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.BMC musculoskeletal disorders · 2026Article
- Application of a YOLOv8-based model on lateral lumbar radiographs for screening of acute and chronic thoracolumbar fractures and osteoporosis.BMC musculoskeletal disorders · 2026Article
- Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.Abdominal radiology (New York) · 2026Article
- Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae.Journal of imaging informatics in medicine · 2026Article
- Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.Current osteoporosis reports · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Sequence-specific radiomics for diagnosis of spinal bone loss.Frontiers in endocrinology · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Diagnostic performance of multimodal imaging integrating X-ray, CT, and MRI in femoroacetabular impingement syndrome.American journal of translational research · 2026Article
- 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
- A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine 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
- Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024Review
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeOsteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging.
methodsPatients who received both lumbar dual-energy X-ray absorptiometry (DEXA) and MRI (n = 120) or CT (n = 100) examinations were included in this study. Unimodal and multimodal convolutional neural networks (CNNs) with dual blocks were proposed to predict osteoporosis using lumbar vertebrae MR and CT examinations in separate and combined datasets. Bone mineral density values obtained by DEXA were used as reference data. The proposed models were compared with a CNN model and six benchmark pre-trained deep-learning models.
resultsThe proposed unimodal model obtained 96.54%, 98.84%, and 96.76% balanced accuracy for MRI, CT, and combined datasets, respectively, while the multimodal model achieved 98.90% balanced accuracy in 5-fold cross-validation experiments. Furthermore, the models obtained 95.68%-97.91% accuracy with a hold-out validation dataset. In addition, comparative experiments demonstrated that the proposed models yielded superior results by providing more effective feature extraction in dual blocks to predict osteoporosis.
conclusionThis study demonstrated that osteoporosis was accurately predicted by the proposed models using both MR and CT images, and a multimodal approach improved the prediction of osteoporosis. With further research involving prospective studies with a larger number of patients, there may be an opportunity to implement these technologies into clinical practice.
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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.