ArticleRadiology. Artificial intelligence2022
Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 4 syntheses or guidelines pooled it, 67 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 in osteoporosis screening and fracture risk prediction among aging populations: a bibliometric evidence map.Frontiers in endocrinology · 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
- Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.Frontiers in medicine · 2025Pooled it
- Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.Advanced healthcare materials · 2026Review
- Opportunistic lumbar vertebral CT attenuation is associated with intertrochanteric femur fracture occurrence and fracture instability in older adults.Skeletal radiology · 2026Article
- Multi-center validation of automated CT-based L1 vertebral Hounsfield unit.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.Abdominal radiology (New York) · 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
- Computed Tomography-Derived visceral fat area is associated with increased risk of kidney stone recurrence.Urolithiasis · 2026Article
- Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.Abdominal radiology (New York) · 2026Article
- Assessing osteosarcopenia using automated multi-structure-segmentation and Hounsfield unit-based analysis of lumbar muscles and vertebral bodies on computed tomography.Scientific reports · 2026Article
- Automated CT-Based Muscle Density Predicts Mortality Regardless of Muscle Area.Journal of cachexia, sarcopenia and muscle · 2026Article
- Opportunistic Screening on Chest CT, From theAJR. American journal of roentgenology · 2026Review
- Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models.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
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- A Novel Nomogram for Predicting Osteoporotic Vertebral Compression Fractures with Hounsfield Unit and Vertebral Bone Quality: A Retrospective Cohort Study.Clinical interventions in aging · 2026Article
- Tumor-Derived Polyamines Initiate Fat Wasting in Cancer Cachexia.bioRxiv : the preprint server for biology · 2025Article
- Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.Abdominal radiology (New York) · 2025Article
- Opportunistic screening for osteoporosis: validation study for L1 bone density measurements using contrast-enhanced chest and abdominal CTs.Skeletal radiology · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Purpose: To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard. Materials and Methods: This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed. Results: The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, Conclusion: The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.
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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.