To establish a convolutional neural network-based liver injury diagnostic model (CNLDM) and evaluate its diagnostic value for liver trauma.

A 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.

The 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.

The 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.