Diagnostic Performance of an Artificial Intelligence Model for Detecting Pediatric Elbow Injuries on Radiographs: A Preliminary Study

Abstract Introduction This study evaluated the capability of a zero-shot AI model to detect bony and soft tissue abnormalities in pediatric elbow radiographs and assessed whether its diagnostic performance could be comparable to that of clinicians. Methods: In this retrospective cohort study, we extracted 2,700 pediatric elbow radiographs from PACS, of which 2,378 met inclusion criteria. These were split into training (1,902), validation (193), and held-out test (169) sets. The AI model (Zen-NAS with ResNet-50 backbone) was trained to detect the presence or absence of pathology based on radiologist reports, using 13 predefined imaging categories (e.g., fractures, effusions, dislocations). Performance was assessed using sensitivity, specificity, and ROC-AUC for binary classification (pathology vs no pathology). Results: The AI model achieved a sensitivity of 87.19%, specificity of 94.12%, and macro-average accuracy of 81.66%. The area under the ROC curve (AUC) was 0.88, indicating strong discriminative performance. Conclusion: This preliminary study demonstrates that a Zen-NAS-based AI model can reliably detect pediatric elbow abnormalities on AP and lateral radiographs. While further validation is required, this technology may offer clinical value as a diagnostic support tool, particularly in settings where specialist radiology services are limited.