Observational studyOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2026
Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.
Observational study in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07100756 (Effectiveness of Implementation of Opportunistic AI-screening for Vertebral Fractures in Clinical Practice - the vertAIdo Project), which is not on this map. Cited by 1 paper.
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.
Effectiveness of Implementation of Opportunistic AI-screening for Vertebral Fractures in Clinical Practice - the vertAIdo Project (Vertebral Fracture AI Detection for Better Osteoporosis Care)
Who cites it
1 citing paper 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We assessed feasibility and effectiveness of AI-based VF screening in CT, integrated with a local FLS. The system identified VFs in 14% of patients, half previously unrecognized or untreated. This suggests that 2-3 patients with VFs were identified daily at our hospital, highlighting the potential clinical impact of AI-assisted detection. PURPOSE: To evaluate the feasibility and efficacy of integrating an AI algorithm into the radiology workflow for opportunistic vertebral fracture (VF) screening in CT and align it to a local fracture liaison service (FLS).
methodsThe AI algorithm was integrated into the radiology workflow and applied to all non-skeletal CT scans covering thorax and/or abdomen from patients aged ≥ 50 years over a four-month period at our hospital (catchment area ~ 250,000). Detected VFs were verified by radiologists and subsequently referred to the FLS for further management. A system was established to enable both technical and clinical monitoring.
resultsThe AI setup and workflow were considered feasible and robust, and AI showed a high performance. During the study period, 3971 unique patients (mean age 72 ± 11 years; 51% female) underwent 5147 CT scans. The AI algorithm identified VFs in 566 patients (14%, mean age 78 ± 10; 62% women), all of which were confirmed by radiologist. After clinical triage, 49% were considered in need of further osteoporosis evaluation/treatment, the remainder where either terminally ill/died shortly after CT or were considered correctly handled before.
conclusionAI-based opportunistic screening for VF is feasible and effective in routine clinical practice. Integration of such tools into radiology workflows enhances the detection of at-risk patients and supports timely referral to FLS, potentially reducing the burden of untreated osteoporosis and future fracture risk. In our clinical setting, this meant 2-3 new identified patients every day. These findings support the broader implementation of AI in secondary fracture prevention strategies. CLINICAL
trial registrationClinicalTrials.gov ID: NCT07100756.
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