Quantitative Evaluation of Real-Time Shear-Wave Elastography under Deep Learning in Children with Chronic Kidney Disease

Abstract

Objective. This research was to study the application value of real-time shear wave elastography (SWE) quantitative evaluation based on deep learning (DL) in the diagnosis of chronic kidney disease (CKD) in children. Methods. 60 children with pathological diagnoses of CKD were selected as a CKD group. During the same period, 45 healthy children for physical examination were selected as the control group. The application value of real-time shear-wave elastography based on DL in the evaluation of CKD in children was explored by comparing the differences between the two groups. Results. It was found that the elastic modulus values of the middle and lower parenchyma of the left kidney and right kidney in the case group were (22.02 ± 10.98) kPa and (21.99 ± 11.87) kPa, respectively, which were substantially higher compared with (4.61 ± 0.47) kPa and (4.50 ± 0.59) kPa in the control group. Young’s modulus (YM) of the middle and lower parenchyma of the left kidney in patients with CKD stages 3 to 5 was 13.27 ± 0.83, 24.21 ± 5.69, and 31.67 ± 3.82, respectively, and that of the right kidney was 17.26 ± 0.98, 26.76 ± 7.22, and 32.37 ± 4.27, respectively, and the difference was significant ( P  < 0.05). In patients with moderate and severe CKD, the YM values of the middle and lower parenchyma of the left kidney were 17.27 ± 0.83, 27.93 ± 6.49, and those of the right kidney were 17.26 ± 0.98, 29.56 ± 6.49, respectively, and the difference was statistically significant ( P  < 0.05). The serum creatinine (Scr) of the CKD group was substantially higher than that of the control group, and the estimated glomerular filtration rate (eGFR) level of the former was lower than that of the latter. However, there was no statistical difference between the YM values of the middle and lower parts of the left and right kidneys of the CKD group and the control group. Conclusion. The DL-based SWE is a new noninvasive, real-time, and quantitative detection method, which can effectively evaluate the stiffness of the kidney and help to better detect the progress of CKD as a clinical reference.