Early detection of chronic kidney disease using deep learning: a Mini review

Area of Science:

• Nephrology
• Artificial Intelligence
• Medical Imaging

Background:

• Chronic Kidney Disease (CKD) is a significant global health burden, often diagnosed late.
• Current diagnostic markers like serum creatinine and eGFR have limitations in early detection.
• Deep learning offers potential for analyzing complex datasets for improved CKD assessment.

Purpose of the Study:

• To review recent advances (2020-2025) in deep learning for early Chronic Kidney Disease (CKD) assessment.
• To highlight the performance of various deep learning models in CKD diagnosis and prediction.
• To identify limitations and future directions for clinical integration of these AI systems.

Main Methods:

• Systematic review of studies published between 2020 and 2025 on deep learning for CKD.
• Analysis of diagnostic accuracies, AUC values, and predictive capabilities of various AI models.
• Comparison of deep learning approaches (CNNs, LSTMs, hybrid, transfer learning) with conventional markers.

Main Results:

• Deep learning models achieved high diagnostic accuracies (88%-99.96%) and AUC values (up to 0.93).
• Ensemble architectures demonstrated predictive power for CKD 6-12 months prior to clinical diagnosis (up to 99.31% accuracy).
• Deep learning models outperformed traditional markers like serum creatinine and eGFR.

Conclusions:

• Deep learning shows significant promise for early CKD detection and prediction.
• Challenges include class imbalance, generalizability, data variability, and model interpretability.
• Future research requires external validation, transparent explanations, and multi-institutional data for clinical adoption.