AI and Machine Learning-Based Decision Support System for Focal Segmental Glomerulosclerosis Management in Nephrology

Abstract

Objective: FSGS represents the chief cause of nephrotic syndrome 
while evolving into terminal kidney failure. Artificial Intelligence 
along with Machine Learning tools support the entire FSGS 
evaluation process while managing treatment to achieve better 
medical decisions for improved patient outcomes.
Methods: The analysis explored AI/ML system applications in 
nephrology scientific study. The research scope included AI/ML 
studies in nephrology which were obtained through a combination 
of PubMed and IEEE Xplore and Scopus between 2015 and 2024.
Results: Through the use of AI/ML algorithms, the accuracy of FSGS 
diagnoses is enhanced, and automated predictions regarding 
prognosis along with tailored treatment strategies see improvements. 
Machine learning models provide superior diagnostic results 
compared to traditional methods by analyzing patient data alongside 
genetic markers and imaging outcomes.
Conclusion: The approach to managing FSGS will be transformed by 
AI and ML technologies that provide rapid and accurate assessments, 
improve treatment plans, and predict potential outcomes. For these 
FSGS management solutions to be effectively adopted in clinical 
practice, it is crucial to address data reliability concerns and enhance 
the clarity of models while integrating them into clinical workflows.