Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.

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. 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). 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. 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. 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. ClinicalTrials.gov ID: NCT07100756.