Abdominal computed tomography (CT) scan is a crucial imaging modality for creating cross-sectional images of the abdominal area, particularly in cases of abdominal trauma, which is commonly encountered in traumatic injuries. However, interpreting CT images is a challenge, especially in emergency. Therefore, we developed a novel deep learning algorithm-based detection method for the initial screening of abdominal internal organ injuries. We utilized a dataset provided by the Kaggle competition, comprising 3,147 patients, of which 855 were diagnosed with abdominal trauma, accounting for 27.16% of the total patient population. Following image data pre-processing, we employed a 2D semantic segmentation model to segment the images and constructed a 2.5D classification model to assess the probability of injury for each organ. Subsequently, we evaluated the algorithm’s performance using 5k-fold cross-validation. With particularly noteworthy performance in detecting renal injury on abdominal CT scans, we achieved an acceptable accuracy of 0.932 (with a positive predictive value (PPV) of 0.888, negative predictive value (NPV) of 0.943, sensitivity of 0.887, and specificity of 0.944). Furthermore, the accuracy for liver injury detection was 0.873 (with PPV of 0.789, NPV of 0.895, sensitivity of 0.789, and specificity of 0.895), while for spleen injury, it was 0.771 (with PPV of 0.630, NPV of 0.814, sensitivity of 0.626, and specificity of 0.816). The deep learning model demonstrated the capability to identify multiple organ injuries simultaneously on CT scans and holds potential for application in preliminary screening and adjunctive diagnosis of trauma cases beyond abdominal injuries.

中文翻译：

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深度学习在腹部创伤CT影像诊断中的应用

腹部计算机断层扫描 (CT) 扫描是创建腹部横截面图像的重要成像方式，特别是在腹部创伤的情况下，这在外伤中很常见。然而，解读 CT 图像是一项挑战，尤其是在紧急情况下。因此，我们开发了一种基于深度学习算法的新型检测方法，用于腹部内脏器官损伤的初步筛查。我们使用了 Kaggle 竞赛提供的数据集，包括 3,147 名患者，其中 855 名被诊断为腹部创伤，占患者总数的 27.16%。在图像数据预处理之后，我们采用 2D 语义分割模型对图像进行分割，并构建 2.5D 分类模型来评估每个器官受伤的概率。随后，我们使用 5k 倍交叉验证评估了算法的性能。在腹部 CT 扫描检测肾损伤方面的表现尤其值得注意，我们达到了 0.932 的可接受准确度（阳性预测值 (PPV) 为 0.888，阴性预测值 (NPV) 为 0.943，敏感性为 0.887，特异性为 0.944） 。此外，肝损伤检测的准确度为0.873（PPV为0.789，NPV为0.895，灵敏度为0.789，特异性为0.895），而脾损伤检测的准确度为0.771（PPV为0.630，NPV为0.814，灵敏度为0.626，特异性为 0.816）。深度学习模型展示了在 CT 扫描中同时识别多个器官损伤的能力，并具有在腹部损伤以外的创伤病例的初步筛查和辅助诊断中应用的潜力。