Wang Julie, Wood Alexander, Gao Chao, Najarian Kayvan, Gryak Jonathan
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Entropy (Basel). 2021 Mar 24;23(4):382. doi: 10.3390/e23040382.
The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, -nearest neighbors (-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.
脾脏是钝性腹部创伤中最常受损的器官之一。计算机断层扫描（CT）是评估钝性脾外伤患者的首选成像方式，钝性脾外伤可能包括撕裂伤、包膜下或实质内血肿、活动性出血以及血管损伤。虽然存在用于通过CT扫描评估的其他病症的计算机辅助诊断系统，但目前检测脾损伤的方法是由放射科医生手动查看扫描图像，这是一个耗时且重复的过程。在本研究中，我们提出了一种使用机器学习的自动脾损伤检测方法。从密歇根医学中心和碰撞损伤研究工程网络（CIREN）数据集中收集了遭受创伤性损伤患者的CT扫描图像。将99张健康脾脏和撕裂脾脏的扫描图像分为不相交的训练集和测试集，通过五折交叉验证训练随机森林（RF）、朴素贝叶斯、支持向量机、k近邻（k-NN）集成模型和子空间判别集成模型。在这些模型中，随机森林表现最佳，在测试集上的受试者操作特征曲线下面积（AUC）达到0.91，F1分数达到0.80。这些结果表明，对创伤性脾损伤进行自动、定量评估有可能实现更快的分诊并改善患者预后。
