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.
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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
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4 authors.
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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.
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