ArticleScientific reports2024
Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Deep Learning-Assisted Automated Diagnosis of Osteoporosis Based on Computed Tomography Scans: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Opportunistic CT-based osteoporosis screening of the hip: a systematic review of diagnostic accuracy.Archives of orthopaedic and trauma surgery · 2025Pooled it
- Opportunistic lumbar vertebral CT attenuation is associated with intertrochanteric femur fracture occurrence and fracture instability in older adults.Skeletal radiology · 2026Article
- Artifact-Controlled Multi-Vertebral Transfer Learning Ensemble for Patient-Level Three-Class Osteoporosis Screening Using Thoracoabdominal CT-Derived Lumbar Images.Biomedicines · 2026Article
- Beyond Bone Density Alone: Opportunistic Identification of Vertebral Compression Fractures in Breast Cancer Survivors Using Artificial Intelligence-Derived Vertebral Bone Density and Paraspinal Muscle-Fat Metrics.Journal of clinical medicine · 2026Article
- Knee-specific bone quality assessment for cementless total knee arthroplasty candidate selection: a narrative review.Knee surgery & related research · 2026Review
- Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Multi-Stage CNN Framework with External Validation.Journal of clinical medicine · 2026Article
- Which vertebral levels provide practical HU-based assessment for osteoporosis screening? A comparative analysis from T1 to S2.Journal of orthopaedic surgery and research · 2026Article
- Opportunistic Screening for Low Bone Density Using Automated Vertebral Trabecular CT Attenuation from Low-Dose CT Acquired During FDG PET/CT: A Single-Center Retrospective Study.Tomography (Ann Arbor, Mich.) · 2026Article
- Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.Abdominal radiology (New York) · 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
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Performance evaluation of deep learning-based osteoporosis diagnostic models with conventional chest X-ray in a clinical cohort.Journal of thoracic disease · 2025Article
- Deep Learning-Based Diagnosis of Lumbar Spondylolisthesis Using X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Incorporating Artificial Intelligence into Fracture Risk Assessment: Using Clinical Imaging to Predict the Unpredictable.Endocrinology and metabolism (Seoul, Korea) · 2025Review
- A comparative study of bone density in elderly people measured with AI and QCT.Frontiers in artificial intelligence · 2025Article
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6 authors.
Funding
Abstract
This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson's correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD and DL-BMD in total CT scans (r = 0.953, p < 0.001). The diagnostic performance of DL-BMD was assessed using receiver operating characteristic analysis for osteoporosis and low BMD by dual-energy x-ray absorptiometry (DXA) and m-BMD. Compared to DXA, DL-BMD demonstrated an AUC of 0.790 (95% CI 0.733-0.839) for low BMD and 0.769 (95% CI 0.710-0.820) for osteoporosis, with sensitivity, specificity, and accuracy of 80.8% (95% CI 74.2-86.3%), 56.3% (95% CI 43.4-68.6%), and 74.3% (95% CI 68.3-79.7%) for low BMD and 65.4% (95% CI 50.9-78.0%), 70.9% (95% CI 63.8-77.3%), and 69.7% (95% CI 63.5-75.4%) for osteoporosis, respectively. Compared to m-BMD, DL-BMD showed an AUC of 0.983 (95% CI 0.973-0.993) for low BMD and 0.972 (95% CI 0.958-0.987) for osteoporosis, with sensitivity, specificity, and accuracy of 97.3% (95% CI 94.5-98.9%), 85.2% (95% CI 78.8-90.3%), and 92.7% (95% CI 89.7-95.0%) for low BMD and 94.4% (95% CI 88.3-97.9%), 89.5% (95% CI 85.6-92.7%), and 90.8% (95% CI 87.6-93.4%) for osteoporosis, respectively. The DL-based method can provide accurate and reliable BMD assessments across diverse CT protocols and scanners.
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