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A High-throughput Method for Measurement of Glomerular Filtration Rate in Conscious Mice
Published on: May 10, 2013
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AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast
Yiwei Wang1, Feng Xu2, Qiuyue Han3
1Department of Urology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Insights Into Imaging
|April 7, 2025
Summary
Artificial intelligence (AI) with non-contrast CT can estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF) in patients with kidney conditions. This approach offers a feasible alternative to SPECT, reducing radiation exposure and costs.
Area of Science:
- Radiology
- Artificial Intelligence
- Renal Function Assessment
Background:
- Single Photon Emission Computed Tomography (SPECT) is currently used for renal function assessment but is limited by radioactivity, complexity, and cost.
- There is a need for alternative, efficient, and cost-effective methods to measure kidney function, particularly split renal function (SRF) and single-kidney glomerular filtration rate (GFR).
Purpose of the Study:
- To develop and validate artificial intelligence (AI) models using non-contrast computed tomography (CT) for estimating single-kidney GFR and SRF.
- To address the limitations of SPECT by offering a potentially safer, faster, and more economical method for renal function assessment in patients with atrophic kidney or hydronephrosis.
Main Methods:
- Deep learning was employed for automatic segmentation of renal parenchyma and hydronephrosis regions in non-contrast CT scans from 245 patients across two centers.
- Radiomic features and clinical data were integrated using multivariable linear regression (MLR) to derive radiomics-clinical-estimated GFR (rcGFR).
- SRF was estimated (rcphSRF) by combining single-kidney rcGFR, percent renal parenchymal volume, and percent renal hydronephrosis volume using MLR.
Main Results:
- Deep learning-based automatic segmentation significantly reduced segmentation time by 434.6 times compared to manual methods.
- Estimated single-kidney GFR (rcGFR) showed strong correlation with SPECT measurements (r=0.75, MAE=10.66 mL/min/1.73 m², CCC=0.70).
- Estimated split renal function (rcphSRF) demonstrated high agreement with SPECT (r=0.92, MAE=7.87%, CCC=0.88).
Conclusions:
- Non-contrast CT combined with AI provides a feasible and efficient method for estimating single-kidney GFR and SRF in patients with atrophic kidney or hydronephrosis.
- The proposed AI-driven approach minimizes radiation exposure, enhances diagnostic efficiency, and reduces costs compared to traditional SPECT imaging.
- The study validates the potential of AI in revolutionizing renal function assessment, offering a promising alternative for clinical practice.
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