AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT.

ABSTRACT:

Objectives

To address SPECT's radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF).

Methods

245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning. Radiomic features were extracted and combined with clinical characteristics using multivariable linear regression (MLR) to obtain a radiomics-clinical-estimated GFR (rcGFR). The relative contribution of single-kidney rcGFR to overall rc

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11977085 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature