Innovative technologies for the identifying risks of chronic kidney disease

Chronic kidney disease (CKD) represents a growing global health burden, affecting more than 10% of the adult population worldwide and significantly increasing morbidity, mortality, and healthcare costs. Early identification of individuals at risk of CKD progression remains a major clinical challenge, as traditional diagnostic approaches based on serum creatinine and estimated glomerular filtration rate (eGFR) often detect disease only at advanced stages. Recent advances in innovative technologies—including novel biomarkers, artificial intelligence (AI), omics sciences, digital health tools, and advanced imaging—have transformed risk stratification and early detection of CKD. This review summarizes current and emerging technologies for identifying CKD risk, highlighting their clinical relevance, limitations, and future perspectives. Integration of these innovative tools into routine clinical practice holds promise for improving early diagnosis, personalized risk prediction, and prevention of CKD progression.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-31
DOI
https://doi.org/10.5281/zenodo.18402064
Primary Topic
Chronic Kidney Disease and Diabetes
Type
article
Field-Weighted Citation Impact
0.00
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article

Innovative technologies for the identifying risks of chronic kidney disease

Kayumov Nodrbek Ulugbekovich, Daminov Botir Turgunpulatovich
Zenodo (CERN European Organization for Nuclear Research)
Chronic Kidney Disease and Diabetes
article

Innovative technologies for the identifying risks of chronic kidney disease

Kayumov Nodrbek Ulugbekovich, Daminov Botir Turgunpulatovich
article en

Abstract

Chronic kidney disease (CKD) represents a growing global health burden, affecting more than 10% of the adult population worldwide and significantly increasing morbidity, mortality, and healthcare costs. Early identification of individuals at risk of CKD progression remains a major clinical challenge, as traditional diagnostic approaches based on serum creatinine and estimated glomerular filtration rate (eGFR) often detect disease only at advanced stages. Recent advances in innovative technologies—including novel biomarkers, artificial intelligence (AI), omics sciences, digital health tools, and advanced imaging—have transformed risk stratification and early detection of CKD. This review summarizes current and emerging technologies for identifying CKD risk, highlighting their clinical relevance, limitations, and future perspectives. Integration of these innovative tools into routine clinical practice holds promise for improving early diagnosis, personalized risk prediction, and prevention of CKD progression.

Zenodo (CERN European Organization for Nuclear Research)
Tashkent Pediatric Medical Institute (UZ)
Openalex Percentile: Top 27%
Chronic Kidney Disease and Diabetes
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Innovative technologies for the identifying risks of chronic kidney disease — Kayumov Nodrbek Ulugbekovich, Daminov Botir Turgunpulatovich · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS