Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment

Area of Science:

• Nephrology
• Medical Informatics
• Artificial Intelligence

Background:

• Chronic kidney disease (CKD) is a growing global health issue.
• Current risk assessment tools for CKD lack precision and generalizability.
• Aging populations, diabetes, and hypertension contribute to rising CKD prevalence.

Purpose of the Study:

• To review the role of artificial intelligence (AI) in CKD detection, progression prediction, and personalized management.
• To highlight AI's potential to overcome limitations of conventional CKD assessment tools.
• To discuss AI applications in early screening, biomarker discovery, and treatment guidance.

Main Methods:

• Review of recent evidence on AI applications in nephrology.
• Analysis of AI's ability to process high-dimensional data (EHR, imaging, omics, wearables).
• Examination of machine learning, deep learning, NLP, and multimodal data integration techniques.

Main Results:

• AI models achieve high accuracy (AUC 0.85-0.96) in predicting CKD outcomes like end-stage kidney disease.
• AI facilitates early CKD screening, biomarker discovery, and precision phenotyping.
• AI aids in guiding personalized interventions, including SGLT2 inhibitor therapy and dialysis initiation.

Conclusions:

• AI demonstrates significant potential to revolutionize CKD care.
• Addressing challenges like algorithmic bias and data privacy is crucial for equitable AI implementation.
• Explainable AI and federated learning are key strategies for overcoming implementation hurdles.