基于深度学习的肝外伤超声图像自动识别模型

目的建立基于深度学习的卷积神经网络肝损伤模型（CNLDM），并评估其对肝实质挫裂伤的诊断价值。 方法通过动物实验获得2009张含有肝实质挫裂伤超声图像及1302张正常肝超声图像，作为模型的训练集和验证集。回顾性收集2015年1月至2021年4月解放军总医院第一医学中心确诊存在肝实质挫裂伤的超声图像153张，以及81张不含肝实质挫裂伤的肝超声图像，作为模型的外部测试集。6名不同年资医师分别对测试集图像数据进行判读。使用受试者操作特征（ROC）曲线及决策曲线分析（DCA）检验模型效能，比较不同年资医师与CNLDM模型预测肝实质挫裂伤的敏感度、特异度、准确性、阴性预测值及阳性预测值。 结果CNLDM模型诊断效能（敏感度为80%，特异度为77%，阳性预测值为87%，阴性预测值为66%）优于低年资医师组（敏感度为61%，特异度为75%，阳性预测值为82%，阴性预测值为51%），略差于高年资医师组（敏感度为84%，特异度为86%，阳性预测值为92%，阴性预测值为75%），差异具有统计学意义（H=15.306，P＜0.001；H=3.289，P＜0.001），而模型效能与中年资医师组接近，差异无统计学意义（P＞0.05）。DCA显示模型在阈值0.4~0.6之间有较好的测试集收益。 结论基于超声的人工智能模型可以较为准确地区分正常肝与含有肝实质挫裂伤的异常肝，对进一步指导临床诊治工作具有重要的意义。

An automatic deep learning-based recognition model for liver trauma ultrasound images

ObjectiveTo establish a convolutional neural network-based liver injury diagnostic model (CNLDM) and evaluate its diagnostic value for liver trauma. MethodsA total of 2009 ultrasound images of liver trauma and 1302 ultrasound images of normal liver were obtained through animal experiments, which were used as the training set and validation set of the model. As an external test set of the model, a retrospective collection of 153 ultrasound images of liver trauma and 81 liver ultrasound images without liver trauma was performed at the First Medical Center of Chinese PLA General Hospital from January 2015 to April 2021. Six doctors of different seniority interpreted the external test set, respectively. Receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to test the performance of the model, and the differences between the six physicians and CNLDM model in sensitivity, specificity, accuracy, negative predictive value, and positive predictive value of liver trauma were compared. ResultsThe diagnostic performance of the CNLDM (sensitivity, 80%; specificity, 77%; positive predictive value, 87%; negative predictive value, 66%) was better than that of the junior physician group (sensitivity, 61%; specificity, 75%; positive predictive value, 82%; negative predictive value, 51%) (H=15.306, P＜0.001), inferior to that of the senior physician group (sensitivity, 84%; specificity, 86%; positive predictive value, 92%; negative predictive value, 75%) (H=3.289, P＜0.001), and close to that of the medium physician group (P＞0.05). DCA showed that the model had good test set returns when the threshold was between 0.4-0.6. ConclusionThe artificial intelligence-based ultrasound model can accurately distinguish normal liver from abnormal liver with trauma, which is of great significance for further guiding clinical diagnosis and treatment.