{Reference Type}: Journal Article
{Title}: Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.
{Author}: Tian S;Yu Y;Shi K;Jiang Y;Song H;Wang Y;Yan X;Zhong Y;Shao G;
{Journal}: Nephrology (Carlton)
{Volume}: 0
{Issue}: 0
{Year}: 2024 Aug 12
{Factor}: 2.358
{DOI}: 10.1111/nep.14376
{Abstract}: <B>OBJECTIVE: </B>This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images.<BR><B>METHODS: </B>According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians.<BR><B>RESULTS: </B>When diagnosing CKD or non-CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p < .05). For the diagnosis of CKD stage, the AUC of our model for CKD G1-G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1-G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p < .05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4-G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4-G5 was 0.838 and 0.963, respectively (all p > .05).<BR><B>CONCLUSIONS: </B>Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD.