{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang Y"],"funding":["the 2023 Whole Laboratory Automation System and Intelligent Analysis System Project","he Key Research and Development Program of Shandong Province","Shanghai Jiaotong University School of Medicine Shanghai high-level local university construction project","the Suzhou Science and Technology Plan Project","the Taishan Industrial Experts Program","the Key Research and Development Program of Jiangsu Province"],"pagination":["84"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11977085"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(1)"],"pubmed_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"],"journal":["Insights into imaging"],"pubmed_title":["AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT."],"pmcid":["PMC11977085"],"funding_grant_id":["tscx202312131","ZTZB-23-990-008","BE2021663, BE2023714","2021SFGC0104","2022zxy005","SJC2021014, SZS2022008"],"pubmed_authors":["Geng D","Han Q","Xu B","Xu F","Gao X","Wang Y","Xia W"],"additional_accession":[]},"is_claimable":false,"name":"AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-13T03:04:58.542Z","creation":"2025-07-13T03:04:58.542Z"},"accession":"S-EPMC11977085","cross_references":{"pubmed":["40192862"],"doi":["10.1186/s13244-025-01959-x"]}}