Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning.

ABSTRACT: Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annot

SUBMITTER: Kuo CC 

PROVIDER: S-EPMC6550224 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature