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

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.

[1]  Shuxuan Xie,et al.  Multi-Disease Prediction Based on Deep Learning: A Survey , 2021 .

[2]  Kumaradevan Punithakumar,et al.  Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks , 2020, Comput. Biol. Medicine.

[3]  F. Hou,et al.  The role of real-time shear wave elastography in the diagnosis of idiopathic nephrotic syndrome and evaluation of the curative effect , 2020, Abdominal Radiology.

[4]  G. Ferraioli,et al.  Ultrasound liver elastography beyond liver fibrosis assessment , 2020, World journal of gastroenterology.

[5]  A. Bello,et al.  Preventing CKD in Developed Countries , 2019, Kidney international reports.

[6]  S. Hou,et al.  Menopause in CKD. , 2018, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[7]  A. Ortiz,et al.  Targeting the progression of chronic kidney disease , 2020, Nature Reviews Nephrology.

[8]  M. Meola,et al.  Ultrasound and color Doppler applications in chronic kidney disease , 2018, Journal of Nephrology.

[9]  Sheida Nabavi,et al.  Convolutional neural network for automated mass segmentation in mammography , 2020, BMC bioinformatics.

[10]  Yan Luo,et al.  Changes in Muscle Mass in Patients With Renal Transplants Based on Ultrasound , 2020, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[11]  M. Reed,et al.  Acute Kidney Injury After CT in Emergency Patients with Chronic Kidney Disease: A Propensity Score-matched Analysis , 2021, The western journal of emergency medicine.

[12]  Junli Zhao,et al.  Medical image fusion method by deep learning , 2021 .

[13]  B. Taouli,et al.  Magnetic resonance elastography vs. point shear wave ultrasound elastography for the assessment of renal allograft dysfunction. , 2020, European journal of radiology.

[14]  Y. Hirooka,et al.  Measurements of renal shear wave velocities in chronic kidney disease patients , 2018, Acta radiologica.

[15]  Zhendong Feng,et al.  Advances of the experimental models of idiopathic membranous nephropathy , 2020, Molecular medicine reports.

[16]  Loretta Ichim,et al.  Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions † , 2020, Sensors.

[17]  Douglas A. Simonetto,et al.  Hepatorenal syndrome: pathophysiology, diagnosis, and management , 2020, BMJ.

[18]  Y. Kulkarni,et al.  Attenuation of renal damage in type I diabetic rats by umbelliferone — a coumarin derivative , 2017, Pharmacological reports : PR.

[19]  Jin Qi,et al.  Dynamic Pixel-wise Weighting-based Fully Convolutional Neural Networks for Left Ventricle Segmentation in Short-axis MRI. , 2020, Magnetic resonance imaging.

[20]  Jason M Misurac,et al.  Chronic kidney disease in the neonate: etiologies, management, and outcomes. , 2017, Seminars in fetal & neonatal medicine.