ArticleAbdominal radiology (New York)2026
Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence, explainable deep learning, and generative models for osteoporosis and bone disease prediction: a systematic review and future research roadmap.Frontiers in artificial intelligence · 2026Pooled it
- 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
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs.
methodsThis is a diagnostic accuracy study with retrospective data from January 2022 to July 2022 consisting of two steps: a segmentation step of the lumbar vertebral bodies, involving outpatient non-contrast abdominal CTs from Diagnósticos da América S/A (Dasa), and a multicenter validation step incorporating data from four additional institutions. The segmentation employed a 2D UNet with a ResNet34 backbone. We determined the Pearson correlation coefficient (r) between the mean of the slices' mean attenuations (MSMA) on CT scans against the bone mineral density (BMD) on recent DEXA scans, calculated AUCs and performance metrics for osteoporosis prediction, including 95% confidence intervals, and evaluated calibration plots.
resultsThe multicenter validation included 504 participants (median age, 66 years, interquartile range 56-72; 388 women). A linear regression analysis showed an r of 0.62 (95% CI, 0.56-0.67) between MSMA (HU) and BMD (g/cm
conclusionWe developed and performed a multicenter validation of a DL model for osteoporosis prediction on CT.
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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.