This article explores the modern advancements in diagnosing chronic glomerulonephritis (CGN), a progressive kidney disease that can lead to end-stage renal disease (ESRD). It highlights the importance of early and accurate diagnosis for effective disease management. Traditional diagnostic methods, such as urinalysis, serum creatinine levels, and renal biopsy, are still widely used; however, recent technological advancements have greatly improved the accuracy and non-invasiveness of CGN diagnostics. The article focuses on various biomarkers (such as Neutrophil Gelatinase-Associated Lipocalin, Kidney Injury Molecule-1, and transforming growth factor-beta), proteomics, genomics, and advanced imaging techniques, including multiparametric MRI, ultrasound elastography, and PET-CT. Additionally, it examines the role of artificial intelligence (AI) and machine learning in automating diagnosis and predicting disease progression. These innovations allow for early detection, personalized treatment, and better monitoring of CGN. Ultimately, the integration of these technologies aims to improve patient outcomes by reducing the need for invasive diagnostic procedures.

Chronic glomerulonephritis (CGN), end-stage renal disease (ESRD), diagnostic methods, renal biopsy, biomarkers, artificial intelligence (AI), NGAL, KIM-1, TGF-β, microRNAs (miRNAs), proteomics, genomics, multiparametric MRI, DWI, magnetization Transfer MRI, ultrasound elastography, PET-CT, fibrosis tracers, AI, machine learning.

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