Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies

doi:10.5527/wjn.v15.i2.117719

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World Journal of Nephrology

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2220-6124

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Nephrol. Jun 25, 2026; 15(2): 117719
Published online Jun 25, 2026. doi: 10.5527/wjn.v15.i2.117719

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Figure 2 Dynamic prediction of chronic kidney disease progression using longitudinal data. Illustrative comparison of static baseline risk estimation vs artificial intelligence based models that update risk over time using longitudinal estimated glomerular filtration rate trajectories, enabling earlier identification of rapid progressors and accounting for competing risks such as death vs progression to end-stage kidney disease. AI: Artificial intelligence; eGFR: Estimated glomerular filtration rate; ESRD: End-stage renal disease.

Citation: Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719
URL: https://www.wjgnet.com/2220-6124/full/v15/i2/117719.htm
DOI: https://dx.doi.org/10.5527/wjn.v15.i2.117719

Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719 [PMID: 42395685 DOI: 10.5527/wjn.v15.i2.117719]

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