Bidirectional Domain Shift Failure in Radiograph Fracture Classification Across Out-of-Distribution Pediatric and Mixed-Age Multi-Region Data

Deep learning systems for radiographic fracture detection routinely achieve high internal accuracy, yet they rarely undergo external validation across demographically distinct populations, leaving critical generalization gaps unexamined. Pediatric and adult populations represent fundamentally different imaging domains due to developmental anatomy (particularly growth plates (physes) in children), making cross-age validation critical for understanding true model robustness. We trained two ResNet-50 classifiers on publicly available datasets: GRAZPEDWRI-DX (pediatric wrist radiographs, n = 20,327) and FracAtlas (mixed-age multi-region radiographs, n = 4083). Models were evaluated internally and cross-tested bidirectionally on the opposing population. Both models achieved acceptable internal test accuracy (82.2% and 83.7%), yet cross-testing revealed complete discrimination failure: specificity collapsed to 0% in both directions while sensitivity artificially inflated to 100% due to all-positive prediction bias. Grad-CAM analysis identified distinct failure mechanisms: the FracAtlas-trained model misinterpreted normal pediatric physes as fractures, while the pediatric-trained model exhibited texture bias on heterogeneous adult imaging. These results demonstrate that high internal performance provides no assurance of cross-population generalizability and that pediatric–adult domain shifts can trigger catastrophic rather than graduated failure. Mandatory external validation across age-defined populations should be required before clinical deployment of fracture detection AI.