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.

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

“…Convolutional neural networks attained over 90% sensitivity and specificity for detecting solid organ abdominal trauma like spleen, liver, and kidney lesions on CT scans. 9 Additional models achieved up to 97% accuracy in diagnosing distal radius fractures on radiographs, 11 98% sensitivity for hip fractures on pelvic X-rays, 14 and AUC exceeding 0.80 for intracranial hemorrhage detection on head CT scans. 19 Deep learning also shows precision in localizing traumatic findings, with activation mapping techniques precisely pinpointing 95.9% of hip fracture lesions 14 and models consistently highlighting displaced ribs on chest CTs.…”

“… Diagnostics [ 10 ] Michel, Manns, Boudersa, Jaubert, Dupic, Vivien, Burgun, Campeotto, Tsopra Clinical decision support system in emergency telephone triage: A scoping review of technical design, implementation and evaluation. Scoping Review US Identified 19 CDSS for emergency telephone triage, highlighting a mix of knowledge-based[ 9 ] and data-driven[ 7 ] systems, primarily aimed at assisting nurses or non-medical staff with patient orientation and severity assessment. Eleven CDSS were implemented in real-world settings, but only three were integrated with Electronic Health Records (EHR), indicating a gap in leveraging existing health data.…”

“…Identified 19 CDSS for emergency telephone triage, highlighting a mix of knowledge-based[ 9 ] and data-driven[ 7 ] systems, primarily aimed at assisting nurses or non-medical staff with patient orientation and severity assessment.…”

“…Most of the investigated studies demonstrated that CNNs can achieve exceptional diagnostic accuracy, with sensitivity and specificity exceeding 90% for diverse traumatic injuries identifiable on CT, X-ray, and MRI. 9 , 11 , 14 , 19 , 24 However, reliance on single-center, retrospective data risks optimism bias and overfitting, requiring further multi-institutional prospective validation encompassing diverse patient populations and scanners before responsible clinical integration. 9 , 11 , 14 , 19 , 21 , 24 Additional gaps emerge in standardized injury quantification and localization.…”

“… 9 , 11 , 14 , 19 , 24 However, reliance on single-center, retrospective data risks optimism bias and overfitting, requiring further multi-institutional prospective validation encompassing diverse patient populations and scanners before responsible clinical integration. 9 , 11 , 14 , 19 , 21 , 24 Additional gaps emerge in standardized injury quantification and localization. Injury severity scoring models demonstrate comparable performance to validated classifications like ISS but currently suffer from mortality overestimation, requiring refinements to improve calibration.…”