Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease

Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease

Objective Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing...

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Bibliographic Details
Main Authors: Le-hao Wu, Dan Zhao, Jian-Ying Niu, Qiu-Ling Fan, Ai Peng, Cheng-gong Luo, Xiao-qin Zhang, Tian Tang, Chen Yu, Ying-ying Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Renal Failure
Subjects:
Chronic kidney disease
kidney fibrosis
predictive model
machine learning
noninvasive assessment
artificial intelligence
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2025.2489715
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https://www.tandfonline.com/doi/10.1080/0886022X.2025.2489715

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