To evaluate the diagnostic value of the convolution neural network model DenseNet121 based on deep learning in the diagnosis of end-stage renal disease (ESRD).

In this retrospective study, 489 kidney ultrasound images of patients diagnosed with end-stage renal disease from January 1, 2019 to September 30, 2019 and 450 kidney ultrasound images of healthy controls were selected at China-Japan Friendship Hospital. The deep learning-based supervised convolutional neural network model DenseNet121 was used for network training and verification. According to whether it was end-stage renal disease or not, the prediction results of the deep learning-based model were compared with the prediction results of professional imaging physicians. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the deep learning-based model, the accuracy, specificity, sensitivity, and area under the curve (AUC) were used as metrics to compare the performance of the deep learning-based model and professional imaging physicians, and Delong was used to compare the difference of AUC.

The prediction accuracy of professional imaging physicians for end-stage renal disease was 89.36%, the sensitivity was 81.63%, the specificity was 97.77%, and the AUC was 0.897. The prediction accuracy of the deep learning-based convolution neural network model DenseNet121 for end-stage renal disease was 93.51%, the sensitivity was 96.12%, the specificity was 90.66%, and the AUC was 0.934. Compared with professional physicians, the DenseNet121 model had higher diagnostic ability (Z=3.034, P=0.002).

The ultrasonic diagnosis method based on deep learning shows high diagnostic performance, and it has the potential to assist professional imaging physicians in the diagnosis of end-stage renal disease.