Presence of crescents in IgA nephropathy-prediction from ultrasound images using deep learning.
Authors
Affiliations (6)
Affiliations (6)
- Department of Ultrasound, West China School of Medicine, Sichuan University, Sichuan University affiliated Chengdu Second People's Hospital, Chengdu Second People's Hospital, Chengdu, 610000, China.
- Department of Ultrasound, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, 637000, China.
- Department of Nephrology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, 637000, China.
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. [email protected].
- Department of Ultrasound, West China School of Medicine, Sichuan University, Sichuan University affiliated Chengdu Second People's Hospital, Chengdu Second People's Hospital, Chengdu, 610000, China. [email protected].
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. [email protected].
Abstract
This study aimed to develop an ultrasound (US)-based deep learning (DL) model to evaluate the presence of crescents in patients with immunoglobulin A nephropathy (IgAN). We created a training set consisting of 2,682 US images obtained from 931 patients with IgAN at the First Affiliated Hospital of Anhui Medical University. The external testing set included 198 patients from Nanchong Central Hospital based on the same criteria. Five DL models were trained in the training set and tested in the testing set. The performance of each model was evaluated for the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The DenseNet121 model achieved an accuracy of 0.773 in the external testing set for predicting the presence of crescents, with a sensitivity of 57.6% and specificity of 87.9%. Using renal ultrasound imaging data, DL may be able to predict crescent status in IgAN, providing clinicians with a potentially non-invasive means to better understand crescent status in patients with IgAN. Not applicable. The online version contains supplementary material available at 10.1186/s12880-025-01958-w.