PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：腹部计算机断层扫描（CT）扫描是创建腹部区域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,解释CT图像是一个挑战，尤其是在紧急情况下。因此,我们开发了一种新颖的基于深度学习算法的检测方法，用于腹部内脏器官损伤的初步筛查。
方法：我们利用了Kaggle竞赛提供的数据集，包括3147名患者,其中855人被诊断为腹部创伤，占患者总数的27.16%。图像数据预处理后，我们采用2D语义分割模型对图像进行分割,并构建了2.5D分类模型来评估每个器官的损伤概率.随后，我们使用5k倍交叉验证评估了算法的性能。
结果：在腹部CT扫描中检测肾损伤的表现尤其值得注意，我们获得了0.932的可接受准确性(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，肝损伤检测的准确性为0.873（PPV为0.789，NPV为0.895，敏感性为0.789，特异性为0.895），而对于脾脏损伤，它是0.771（PPV为0.630，NPV为0.814，敏感性为0.626，特异性为0.816）。
结论：深度学习模型证明了在CT扫描中同时识别多器官损伤的能力，并有可能应用于腹部损伤以外的创伤病例的初步筛查和辅助诊断。
方法：我们利用了Kaggle竞赛提供的数据集，包括3147名患者,其中855人被诊断为腹部创伤，占患者总数的27.16%。图像数据预处理后，我们采用2D语义分割模型对图像进行分割,并构建了2.5D分类模型来评估每个器官的损伤概率.随后，我们使用5k倍交叉验证评估了算法的性能。
结果：在腹部CT扫描中检测肾损伤的表现尤其值得注意，我们获得了0.932的可接受准确性(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，肝损伤检测的准确性为0.873（PPV为0.789，NPV为0.895，敏感性为0.789，特异性为0.895），而对于脾脏损伤，它是0.771（PPV为0.630，NPV为0.814，敏感性为0.626，特异性为0.816）。
结论：深度学习模型证明了在CT扫描中同时识别多器官损伤的能力，并有可能应用于腹部损伤以外的创伤病例的初步筛查和辅助诊断。
