Convolution Neural Network for Renal Function Assessment Based on Glomerular Filtration Rate

Abstract

Absract ✓Objective: To develop a Convolution Neural Network (CNN) and discuss its performance in the estimation of Glomerular Fifiltration Rate (GFR) for patients with chronic kidney disease (CKD). Methods: A total of 112 patients with chronic kidney disease were enrolled in this study. The GFR is measured by 99mTc-DTPA renal dynamic and used as standard GFR after normalization by body surface area imaging. We established a CNN model and verified the performance of the model by comparing the GFR predicted by the model with the standard GFR. It turned out that the CNN could better evaluate the GFR of patients which is superior to the CG formula, MDRD formula, CKD-EPI formula and GRNN model. Conclusions: The CNN significantly evaluated GFR for patients with CKD, and it showed better performance than traditional methods and GRNN model and closer to the results of 99mTc-DTPA. The experimental results demonstrated that CNN could be used to estimate GFR.

Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

“…The global disparities in kidney disease can be effectively understood through the research proposed by Poonia et al 40 Wu et al 41 developed a CNN along with assessing GFR for patients influenced by CKD. An overall of 112 patients affected with CKD was considered in this research study.…”

“…Wu et al 41 developed a CNN along with assessing GFR for patients influenced by CKD. An overall of 112 patients affected with CKD was considered in this research study.…”