HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE IN PEDIATRIC NEPHROLOGY: A COMPREHENSIVE REVIEW OF EARLY DETECTION, DIAGNOSIS, AND MANAGEMENT OF KIDNEY DISEASES

Abstract

Artificial intelligence (AI) is revolutionizing the field of pediatric nephrology by offering innovative solutions for the early detection, diagnosis, and management of kidney diseases in children and neonates. This narrative review explores AI's current applications and future potential in enhancing the care of pediatric patients with acute kidney injury (AKI), chronic kidney disease (CKD), and glomerular diseases. We conducted a comprehensive literature search using Scopus, Web of Science, PubMed, and ScienceDirect to identify relevant studies published between 2015 and 2023. The findings demonstrate that AI models, such as XGBoost and logistic regression, have shown promise in predicting AKI by analyzing variables like serum creatinine and urine output. Integration of these models with electronic health record (EHR) systems has the potential to provide timely alerts and improve patient outcomes. In neonatal care, AI applications like the "Baby NINJA" model have significantly reduced nephrotoxic medication exposure and the incidence of AKI. In contrast, the "STARZ" model has exhibited high predictive accuracy for AKI within the first week of neonatal intensive care unit admission. AI is also being explored for its utility in managing glomerular diseases through digital pathology and enhancing the predictive accuracy of kidney biopsies with deep learning algorithms. However, these technologies are still primarily in the research phase and require further validation for clinical application. This review concludes that AI's integration into pediatric nephrology holds immense promise for improving diagnostic accuracy, personalizing treatment plans, and enhancing predictive assessments, potentially transforming kidney disease management in children and neonates.