Artificial intelligence in chronic kidney diseases: methodology and potential applications.

Area of Science:

• Nephrology
• Cardiology
• Medical Informatics

Background:

• Chronic kidney disease (CKD) is a growing global health concern with increasing prevalence.
• Traditional prognostic markers like eGFR and albuminuria have limitations in fully assessing CKD progression and cardiovascular (CV) risks.
• Accurate risk prediction is crucial for effective CKD management and patient care.

Purpose of the Study:

• To review current renal and CV risk prediction strategies in CKD.
• To highlight the limitations of traditional prognostic models.
• To explore the potential of artificial intelligence (AI) in enhancing CKD risk prediction.

Main Methods:

• Review of existing literature on CKD risk prediction.
• Analysis of traditional prognostic markers (eGFR, albuminuria).
• Exploration of AI techniques, including machine learning (ML) and deep learning (DL), for risk assessment.

Main Results:

• Traditional models may not fully capture CKD complexity and CV risks.
• AI offers a promising approach by analyzing diverse patient data (genetics, biomarkers, imaging) to identify complex patterns.
• AI can generate more comprehensive risk profiles for personalized assessments.

Conclusions:

• AI, particularly ML and DL, holds significant potential for improving renal and CV risk prediction in CKD.
• Challenges to AI integration include algorithmic opacity, data quality, privacy, and bias.
• Explainable AI (XAI) and robust data governance are critical for trustworthy AI implementation in clinical practice.