AI and ML-Driven Decision Support System for Managing Focal Segmental Glomerulosclerosis in Nephrology

Abstract

Focal Segmental Glomerulosclerosis (FSGS) stands as the primary reason behind nephrotic syndrome cases that frequently results in kidney failure reaching its terminal stage. Artificial Intelligence and Machine Learning approaches provide assistance throughout FSGS assessment together with treatment and management while enhancing medical choices to enhance patient results.

Methods: A review analysed how AI/ML functions for nephrology research. Research related to AI/ML in nephrology was retrieved from the databases of PubMed alongside IEEE Xplore and Scopus within the timeframe of 2015 to 2024.

Results: By implementing AI/ML algorithms FSGS diagnosis becomes more accurate while automated prognostic predictions and customized treatment approaches also improve. Machine learning models deliver better diagnostic outcomes than conventional approaches by processing patient data combined with genetic markers and image results through analysis. 

Conclusion: The management of FSGS will be revolutionized through AI and ML technology which delivers quick precise evaluations and enhances therapeutic strategies together with forecasting prognosis scenarios. The successful implementation of FSGS management solutions in clinical practice depends on solving data reliability issues as well as working to improve model readability and clinical practice integration.