Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
Department of Medical Imaging & Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
J Imaging Inform Med. 2024 Jun;37(3):1113-1123. doi: 10.1007/s10278-024-01038-5. Epub 2024 Feb 16.
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.
计算机断层扫描（CT）是钝性腹部创伤（BAT）最常用的诊断方式，显著影响了处理方法。深度学习模型（DLM）在增强临床实践的各个方面显示出巨大的潜力。关于 DLM 专门用于创伤图像评估的应用，目前文献有限。在这项研究中，我们开发了一种 DLM，旨在检测实体器官损伤，以帮助医疗专业人员快速识别危及生命的损伤。该研究纳入了 2008 年至 2017 年期间在单一创伤中心接受腹部 CT 扫描的患者。将脾、肝或肾损伤的患者归类为实体器官损伤组，其他患者则归类为阴性病例。仅纳入来自创伤中心的图像。将去年采集的一部分图像指定为测试集，其余图像用于训练和验证检测模型。根据最佳 Youden 指数工作点，使用受试者工作特征曲线（ROC）下面积（AUC）、准确性、敏感度、特异度、阳性预测值和阴性预测值等指标评估每个模型的性能。该研究使用 1302 次（87%）扫描进行训练，并在 194 次（13%）扫描上进行测试。脾损伤模型的准确性为 0.938，特异性为 0.952。肝损伤模型的准确性和特异性分别为 0.820 和 0.847。肾损伤模型的准确性为 0.959，特异性为 0.989。我们开发了一种 DLM，可以通过腹部 CT 扫描自动检测实体器官损伤，具有可接受的诊断准确性。它不能替代临床医生的作用，但我们可以期望它成为创伤护理中加速治疗决策过程的潜在工具。
