Objective: To investigate the diagnostic efficacy and potential application value of deep learning-based chest CT auxiliary diagnosis system in emergency trauma patients. Methods: A total of 403 patients, including 254 males and 149 females aged from 16 to 100 (50±19) years, who received emergency treatment for trauma and chest CT examination in the Eastern Theater General Hospital from September 2019 to November 2019 were retrospectively analyzed. Dr. Wise Lung Analyzer's chest CT auxiliary diagnosis system was applied to detect 5 types of injuries, including pneumothorax, pleural effusion/hemothorax, pulmonary contusion (shown as consolidation and ground glass opacity), rib fractures, and other fractures (including thoracic vertebrae, sternum, scapula and clavicle, etc.) and 6 other abnormalities (bullae, emphysema, pulmonary nodules, stripe, reticulation, pleural thickening). The diagnostic reference standards were labeled by two radiologists independently. The sensitivity and specificity of the auxiliary diagnosis system were evaluated. The imaging diagnostic reports were compared with the results of the auxiliary diagnosis system, and the diagnostic consistency between the two was calculated by using the Kappa test. Results: According to the reference standards, among the 403 patients, 29 were pneumothorax, 75 were pleural effusion/hemothorax, 131 were pulmonary contusion, 124 were rib fractures, and 63 were other fractures. The sensitivity and specificity of the auxiliary diagnosis system for detection of pneumothorax, pleural effusion/hemothorax, rib fractures, and other fractures were 96.6%, 97.6%, 80.0%, 99.7%, 99.2%, 83.9%, 84.1%, and 99.7%, respectively. The sensitivity of detecting lung contusion was 97.7%. There was a high consistency between the auxiliary diagnosis system and imaging diagnosis in the diagnosis of injuries, in which the kappa values of pneumothorax, pleural effusion, rib fracture and other fractures were 0.783, 0.821, 0.706 and 0.813, respectively (all P<0.001). Two cases of pneumothorax, three cases of pleural effusion/hemothorax, nine cases of rib fractures, and six cases of other fractures missed by imaging diagnosis were all detected by the auxiliary diagnosis system. The detection sensitivity of the auxiliary diagnosis system was higher for emphysema, pulmonary nodules and stripe (all>85%), but lower for bullae, reticulation and pleural thickening. Conclusions: The deep learning-based chest CT auxiliary diagnosis system could effectively assist chest CT to detect injuries in emergency trauma patients, which was expected to optimize the clinical workflow.
目的： 探讨深度学习胸部CT辅助诊断系统在急诊创伤患者中的诊断效能及潜在应用价值。 方法： 回顾性连续纳入东部战区总医院2019年9至11月因创伤急诊就诊并且行胸部CT检查的403例患者，男254例，女149例，年龄16~100（50±19）岁。采用Dr.Wise Lung Analyzer的胸部CT辅助诊断系统检测5种损伤，包括气胸、胸腔积液/血、肺挫伤（表现为实变影和磨玻璃影）、肋骨骨折、其他骨折（包括胸椎、胸骨、肩胛骨、锁骨等）及6种其他异常（肺大疱、肺气肿、肺结节、条索影、网格影、胸膜增厚）。由两名影像科医师独立进行标注确定诊断参考标准，计算辅助诊断系统的灵敏度及特异度。另将影像报告诊断结果与辅助诊断系统结果相比较，并采用Kappa检验计算两者的诊断一致性。 结果： 403例患者中参考标准诊断有气胸 29例，胸腔积液/血75例，肺挫伤131例，肋骨骨折124例，其他骨折63例。辅助诊断系统检测气胸、胸腔积液/血、肋骨骨折、其他骨折的灵敏度和特异度分别为96.6%和97.6%、80.0%和99.7%、99.2%和83.9%、84.1%和99.7%，检测肺挫伤的灵敏度为97.7%。辅助诊断系统与影像诊断对于损伤的诊断一致性高，对于气胸、胸腔积液、肋骨骨折、其他骨折的Kappa值分别为0.783、0.821、0.706、0.813（均P<0.001）。影像诊断漏诊气胸2例、胸腔积液/血3例、肋骨骨折9例及其他骨折6例，均被辅助诊断系统检出。辅助诊断系统对于肺气肿、肺结节、条索影的检测灵敏度较高（均>85%），对于肺大疱、网格影及胸膜增厚的检测灵敏度稍低。 结论： 深度学习胸部CT辅助诊断系统能有效辅助检测急诊创伤患者胸部CT中的损伤，有望优化急诊创伤患者的诊疗流程。.