Diagnostic Performance of an Artificial Intelligence Cervical Spine Fracture Decision Support System at a Non-Trauma Community Hospital Setting

Traumatic cervical spine fractures (CSFxs) require timely diagnosis due to associated morbidity. Artificial intelligence (AI)-based decision support systems have been proposed to improve imaging workflow efficiency. However, their performance in non-trauma settings remains unclear. This study evaluated the diagnostic performance of the AIDOC decision support system (DSS) for detecting CSFxs in a non-trauma academic community hospital using a retrospective analysis of 1812 cervical spine CT scans, with radiologist interpretation as the reference standard. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated for fracture detection and heatmap-based localization. The AI system demonstrated a sensitivity of 72.2% and specificity of 98.1%, with an accuracy of 97.9%. In the context of low fracture prevalence (0.99%), PPV was low (27.7%), while NPV was high (99.7%). Heatmap-based localization showed reduced sensitivity (43.8%) despite high specificity (97.5%). These findings demonstrate high specificity and NPV, with lower sensitivity for localization and low PPV in a low-prevalence setting. Prospective multi-institutional studies are required to further validate these diagnostic performance metrics and assess generalizability across diverse clinical settings and imaging protocols.