ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Deep learning model for osteoporosis screening on chest CT with low tube voltage.
Article in 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, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposeThe purpose of this study is to develop a deep learning (DL) model for osteoporosis screening based on images of thoracic vertebrae obtained from chest CT with a low tube-voltage of 100 kV, with quantitative computed tomography (QCT) as a reference standard.
methodsFrom May 2022 to January 2024, a total of 649 patients who underwent low tube-voltage chest CT and lumbar QCT were retrospectively included in this study, with 518 cases in the training set and 131 in the testing set. Patients were classified as normal bone mineral density (BMD), osteopenia and osteoporosis according to QCT. The CT images of T5-T8 were input and DL models were established through a two-level network of Bone-PSPNet and Ost-ClassNet. Receiver operating characteristic curve analysis was used to evaluate the classification performance of the DL models.
resultsFor distinguishing patients with osteoporosis from non-osteoporotic ones (normal BMD and osteopenia), the basic DL model achieved a high sensitivity of 1 and an AUC value of 0.978. For distinguishing low BMD (osteopenia and osteoporosis) from normal BMD, the basic DL model achieved a sensitivity of 0.969 and an AUC value of 0.945.
conclusionThis study developed a DL model based on low tube-voltage chest CT to automatically identify osteoporosis, which has shown good classification performance.
Indexed as
Identifiers
41207963What Socratic holds
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 Socratic graph.