ArticleArchives of osteoporosis2025
Breaking the silence: AI's contribution to detecting vertebral fractures in opportunistic CT scans in the elderly-a validation study.
Article in Archives of osteoporosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Recognition and reporting of vertebral fractures in thoracic and abdominal computed tomography images - have we learnt our lesson?Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Assessing the clinical reliability of deep learning for vertebral fracture detection: a two-surgeon commentary.La Radiologia medica · 2026Article
- Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Observational
- Osteoporosis: a missed link in fall prevention - a retrospective cohort study.BMC geriatrics · 2026Article
- [Artificial intelligence in musculoskeletal radiology : Current advances and emerging frontiers].Orthopadie (Heidelberg, Germany) · 2026Article
- Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity.Frontiers in artificial intelligence · 2026Review
- The role of AI assistance in the evaluation of unenhanced chest CT scans in an emergency setting.Polish journal of radiology · 2026Article
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7 authors.
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Abstract
Vertebral fractures frequently go undetected in clinical practice. AI-assisted detection on CT scans demonstrates considerable promise, with a sensitivity of 86% and a specificity of 99%. The performance varied based on sex, and CT kernel, showing superior results in females and in scans using non-bone kernel protocols. PURPOSE: Vertebral fractures (VFs) are highly underdiagnosed, necessitating the development of new identification methods for opportunistic screening in computed tomography (CT) scans. This study validated an AI algorithm (ImageBiopsy Lab [IBL], FLAMINGO) for detecting VFs in a geriatric cohort, with various subgroup analyses including different CT protocols.
methodsThe performance of the AI in detecting VFs was compared to assessments by two experienced radiologists. A total of 246 thoracic or abdominal CT scans, primarily conducted for purposes other than skeletal examination, were included in the study.
resultsThe patients had a mean age of 84 years (range 62 to 103), with 42% being female. The AI demonstrated high accuracy (0.93), sensitivity (0.86), and specificity (0.99) in detecting moderate to severe VFs. Subgroup analysis revealed accuracy ranging from 0.88 to 0.96, with higher accuracy in females compared to males (0.96 vs. 0.89, p = 0.03) and in scans performed with non-bone kernel versus bone kernel protocols (0.96 vs. 0.88, p = 0.02). No significant differences were found for age, contrast phase, or spinal region.
conclusionThe results indicate that the AI algorithm exhibits high performance in a geriatric setting. If effectively integrated with a fracture liaison service, this could enhance VF detection considerable in the future.
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