Adequacy and accuracy of nontrauma center computed tomography: What are we missing?

External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT

PURPOSE

This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geographically and temporally distinct external dataset.

METHOD

A single-center retrospective study was conducted using an external dataset comprising 1216 CT scans (608 positive and 608 negative for splenic injuries). The ML model, trained on the RSNA Abdominal Traumatic Injury CT (RATIC) dataset, employs a multi-component pipeline including 2D MaxVit, 2.5D CoatNet with LSTM for study-level predictions. Model performance was evaluated using sensitivity, specificity, PPV, NPV, accuracy, F1 score, and AUC.

RESULTS

The ML model achieved an AUC of 0.931 (95% CI: 0.917, 0.945) for binary classification of splenic injuries, with an accuracy of 0.849 (95% CI: 0.827, 0.868), sensitivity of 0.747 (95% CI: 0.711, 0.780), and specificity of 0.951 (95% CI: 0.930, 0.965). For high-grade splenic injuries, the model achieved an AUC of 0.950 (95% CI: 0.932, 0.968), accuracy of 0.928 (95% CI: 0.912, 0.941), sensitivity of 0.719 (95% CI: 0.643, 0.784), and specificity of 0.958 (95% CI: 0.944, 0.968).

CONCLUSION

The ML model shows strong, reliable performance and generalizability in detecting and grading splenic injuries on CT scans. This supports its potential clinical application, particularly for quick and accurate diagnosis in splenic trauma patients, and highlights the value of RSNA AI challenges in advancing clinical research and applications in medical imaging.

The Financial and Radiation Burden of Early Reimaging in Neurosurgical Patients: An Original Study and Review of the Literature

The computed tomographic (CT) scanner has become ubiquitous in healthcare. When trauma patients are imaged at facilities not equipped to care for them, imaging is often repeated at the receiving institution. CTs have clinical, financial, and resource costs, and eliminating unnecessary imaging will benefit patients, providers, and institutions. This paper reviews patterns of repetition of CT scans for transferred trauma patients and motivations underlying such behaviors via analysis of our Trauma Registry database and literature published in this area. Neurosurgeons are fundamentally impactful in this decision-making process. The most commonly repeated scan is a CT head (CTH). More than ¼ of our patients receiving a clinically indicated repeat CTH also had a repeat scan of their cervical spine with no reason given for the cervical scan. Herein, we discuss our findings that both non-trauma center practitioners and non-neurosurgical staff at trauma centers cite a lower level of comfort with neuroradiology and fear of litigation as motivators in overzealous neuroimaging. As a result, inappropriate neurosurgical imaging is routinely ordered prior to transfer and again upon arrival at trauma centers. Education of non-neurosurgical staff is essential to prevent inappropriate neuroaxis imaging.