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). Methods The 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. Results The 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. Conclusion AI-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 registration ClinicalTrials.gov ID: NCT07100756
Woisetschläger et al. (Tue,) studied this question.