To explore the application value of artificial intelligence aided diagnosis model based on convolutional neural network (CNN) in ultrasonic diagnosis of blunt renal trauma.

Rabbits were used to simulate different grades of renal trauma model of renal trauma of different degrees was established. The ultrasonic images of the normal kidney and renal trauma were collected by point of care ultrasound (POCUS) and divided into either a training or a test cohort. According to the modeling position and contrast-enhanced ultrasound results, the renal contour was manually drawn and classified for training, followed by 3-fold cross-validation testing. The sensitivity, specificity, accuracy, and area under curve (AUC) of the artificial intelligence aided diagnosis model were calculated.

A total of 1737 images of the normal kidney and 2125 images of traumatic kidney were collected. After the verification of the test set, the model can automatically classify the presence or absence of renal trauma. The average sensitivity for renal trauma diagnosis was 73%, the average specificity was 85%, the average accuracy was 79%, and the AUC was 0.80.

The deep learning assisted POCUS model constructed based on CNN has achieved satisfactory results in the diagnosis and classification of renal trauma.