ArticleInternational journal of medical sciences2026
Computed Tomography-Verified Deep Learning Model for Rib Fracture Prediction and Complication Association from Chest Radiographs.
Article in International journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Chest radiography (CXR) is the first-line imaging for suspected rib fractures but has limited sensitivity, potentially increasing the risk of thoracic complications. This study aimed to develop and validate a deep learning model (DLM) based on CT-verified labels to predict acute rib fractures on CXRs and evaluate its association with thoracic complications. Methods: This retrospective study included 14,758 patients who underwent chest CT and CXR within seven days. We used 10,818 CXRs for development, 4,377 for tuning, and 3,020 for validation. The DLM employed a Vision Transformer (ViT-B/32) architecture. Performance was assessed using AUC, sensitivity, and predictive values. Risk stratification was performed via Youden index and F-score optimization. Results: The DLM achieved an AUC of 0.874, sensitivity of 84.9%, and negative predictive value (NPV) of 99.4% in validation. Performance remained consistent across care settings (AUCs 0.819-0.853). Subgroup analysis showed the best performance in patients < 60 years (AUC 0.934), while comorbidities were associated with lower AUCs. High-risk patients exhibited significantly higher one-month and one-year incidences of pneumothorax (17.4% and 12.5%) and pulmonary contusion (9.8% and 8.0%) compared to low-risk patients (all p < 0.001). No significant difference was observed for pneumonia association. Conclusions: This CT-verified DLM demonstrated high diagnostic performance for predicting acute rib fractures and effectively stratified patients by associated complication risk. This model may assist clinicians with early prediction and triage, potentially enhancing clinical efficiency and patient outcomes in trauma workflows.
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