Clinical Validation of AI-Powered BUN/Creatinine Ratio and Kidney Function Interpretation: A Multi-Parameter Neural Network Approach for Enhanced Renal Diagnostic Accuracy

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Authors: Thomas Klein, Hans Weber, Sarah Mitchell
Cited by: 0

Abstract

Background: Accurate interpretation of blood urea nitrogen (BUN) to creatinine ratio and associated renal biomarkers is essential for early detection of kidney dysfunction. Traditional interpretation methods rely heavily on clinician expertise and may introduce variability in diagnostic accuracy. This study validates an artificial intelligence (AI) system utilizing a 2.78 trillion parameter neural network for automated kidney function interpretation. Methods: We conducted a retrospective analysis of 1,247,892 blood test results from 127 countries collected between January 2023 and September 2025 through the Kantesti AI platform. The AI system's interpretations were compared against gold-standard assessments by board-certified nephrologists. Results: The AI system demonstrated 98.7% overall diagnostic accuracy (95% CI: 98.4-99.0%) for kidney function interpretation. Agreement with nephrologist assessments showed excellent concordance (Cohen's κ = 0.94). Conclusions: AI-powered interpretation of BUN/creatinine ratio and kidney function biomarkers achieves clinical-grade accuracy comparable to specialist assessment. Article Link: https://www.kantesti.net/bun-creatinine-ratio-kidney-function-guide/

Fields of Study & Subjects

Artificial Intelligence in Healthcare Artificial Intelligence in Healthcare and Education Acute Kidney Injury Research

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
10 January 2026
Language
EN