Deep learning for kidney trauma detection: CT image algorithm performance and external validation: experimental study.

Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.

Methods: We developed RenoTrNet, a DL model trained on institutional data. We evaluated the model's performance through external validation on randomly selected cases from the RSNA dataset. Performance metrics included the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Heatmap visualizations were used to aid interpretability.

Results: In the internal testing dataset, the model achieved an accuracy of 0.88 (95% CI: 0.82-0.92), with a sensitivity of 0.75 (95% CI: 0.62-0.85) and a specificity of 0.95 (95% CI: 0.89-0.98). PPV and NPV were 0.89 (95% CI: 0.76-0.95) and 0.88 (95% CI: 0.81-0.93), respectively. In external RSNA validation, the algorithm c demonstrated robust performance with an accuracy of 0.93 (0.91-0.95), a sensitivity of 0.73 (0.60-0.83), a specificity of 0.94 (0.93-0.96), a PPV of 0.45 (0.35-0.56), and an NPV of 0.98 (0.97-0.99).

Conclusion: The RenoTrNet DL algorithm demonstrated high accuracy in detecting kidney trauma on CT scans, both in internal and external validation. By optimizing image segmentation and computational efficiency, this model has potential for clinical deployment, potentially aiding in trauma diagnosis in real-world clinical scenarios.