ArticleQuantitative imaging in medicine and surgery2023
Development and validation of a fully automated system using deep learning for opportunistic osteoporosis screening using low-dose computed tomography scans.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled 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
- 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 accuracy of an artificial intelligence-based osteoporosis screening system on portable chest radiographs.Journal of thoracic disease · 2026Article
- Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.Abdominal radiology (New York) · 2026Article
- AI-Driven Bone and Marrow Segmentation on FLT-PET/CT: Technical Multi-organ Validation in AML and HCT.Research square · 2026Article
- Deep learning model for osteoporosis screening on chest CT with low tube voltage.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
- Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.Frontiers in artificial intelligence · 2026Article
- Osteoporosis screening and major osteoporotic fracture prediction by cranial computed tomography-derived Hounsfield units: a multi-center study on opportunistic osteoporosis screening.Annals of medicine · 2025Article
- Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners.Scientific reports · 2024Article
- 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
- Automated Opportunistic Osteoporosis Screening Using Low-Dose Chest CT among Individuals Undergoing Lung Cancer Screening in a Korean Population.Diagnostics (Basel, Switzerland) · 2024Article
- Fully automated deep learning system for osteoporosis screening using chest computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Bone density measurement is an important examination for the diagnosis and screening of osteoporosis. The aim of this study was to develop a deep learning (DL) system for automatic measurement of bone mineral density (BMD) for osteoporosis screening using low-dose computed tomography (LDCT) images. Methods: This retrospective study included 500 individuals who underwent LDCT scanning from April 2018 to July 2021. All images were manually annotated by a radiologist for the cancellous bone of target vertebrae and post-processed using quantitative computed tomography (QCT) software to identify osteoporosis. Patients were divided into the training, validation, and testing sets in a ratio of 6:2:2 using a 4-fold cross validation method. A localization model using faster region-based convolutional neural network (R-CNN) was trained to identify and locate the target vertebrae (T12-L2), then a 3-dimensional (3D) AnatomyNet was trained to finely segment the cancellous bone of target vertebrae in the localized image. A 3D DenseNet was applied for calculating BMD. The Dice coefficient was used to evaluate segmentation performance. Linear regression and Bland-Altman (BA) analyses were performed to compare the calculated BMD values using the proposed system with QCT. The diagnostic performance of the system for osteoporosis and osteopenia was evaluated with receiver operating characteristic (ROC) curve analysis. Results: Our segmentation model achieved a mean Dice coefficient of 0.95, with Dice coefficients greater than 0.9 accounting for 96.6%. The correlation coefficient (R2) and mean errors between the proposed system and QCT in the testing set were 0.967 and 2.21 mg/cm Conclusions: The fully automated DL-based system is able to perform automatic BMD calculation for opportunistic osteoporosis screening with high accuracy using LDCT scans.
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