Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans

Abstract

Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma care.

Keywords: Artificial intelligence; Blunt abdominal trauma; Computed tomography; Deep learning; Liver injury; Renal injury; Spleen injury.

Fig. 1

Dataset preparation

Fig. 2

A comprehensive overview of the algorithm design for solid organ injury detection

Fig. 3

The ROC curve and AUROC of each solid organ injury detection model. A Whole image model. B Spleen injury model. C Liver injury model. D Kidney injury model. The shaded area represented the 95% confidence interval

Fig. 4

Visualization examples of each solid organ injury detection model. A The heatmap from the whole image model indicates a failure to localize the spleen injury. B The heatmap from the spleen injury model accurately highlights the lacerated area and hematoma surrounding the spleen. C The heatmap of the liver injury model successfully localizes a grade 3 injury. D The heatmap points out the laceration in the kidney and the presence of a perirenal hematoma