International Journal of Science, Engineering and TechnologyAuthors: Aiysha SameenaV, Dr Rajkumar R
Abstract: Pediatric nephrology faces unique challenges in the early detection, diagnosis, and management of kidney diseases due to the subtlety of symptoms and the need for specialized expertise. The integration of Artificial Intelligence (AI) offers promising avenues to enhance clinical decision-making, improve diagnostic accuracy, and personalize treatment strategies in this specialized field. This comprehensive review aims to evaluate the current applications, methodologies, and future prospects of AI in pediatric nephrology, focusing on its role in early detection, diagnosis, and management of kidney diseases in children. A systematic literature search was conducted across databases including Scopus, Web of Science, PubMed, and ScienceDirect to identify relevant studies published between 2015 and 2023. The review encompasses various AI methodologies such as machine learning algorithms (e.g., XGBoost, logistic regression), deep learning models, and their integration with electronic health records (EHRs). The analysis includes studies on AI-assisted histopathological image analysis, predictive modeling for acute kidney injury (AKI), chronic kidney disease (CKD), and other glomerular diseases in pediatric populations. The review highlights that AI models have demonstrated efficacy in predicting AKI by analyzing variables like serum creatinine and urine output, with some models achieving high accuracy rates. Integration of AI with EHR systems has shown potential in providing timely alerts, thereby improving patient outcomes. AI-assisted image analysis tools have enhanced the accuracy and efficiency of diagnosing various kidney pathologies. However, challenges such as data quality, algorithmic bias, and the need for domain-specific training remain prevalent.
DOI: https://doi.org/10.5281/zenodo.17863132
