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

## TL;DR

This paper reviews how deep learning improves early detection of chronic kidney disease using advanced models and data analysis.

## Contribution

The mini-review highlights recent deep learning models achieving high accuracy in early CKD detection.

## Key findings

- Deep learning models report diagnostic accuracies from 88% to 99.96% for CKD.
- Ensemble architectures can predict CKD up to 12 months before diagnosis with 99.31% accuracy.
- Current models outperform traditional diagnostic methods like serum creatinine and eGFR.

## Abstract

Chronic Kidney Disease (CKD) remains a major contributor to global morbidity, often progressing unnoticed until advanced stages when treatment options become limited and costly. Recent advances in deep learning have reshaped early CKD assessment by enabling the analysis of complex imaging, clinical, and longitudinal laboratory datasets. This mini-review synthesizes findings from studies published between 2020 and 2025, highlighting models that report diagnostic accuracies ranging from 88% to 99.96%, AUC values reaching 0.93, and ensemble architectures capable of forecasting CKD 6 to12 months before clinical diagnosis with up to 99.31% accuracy. These systems spanning Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), hybrid CNN–LSTM designs, and transfer-learning frameworks have demonstrated clear advantages over conventional diagnostic markers such as serum creatinine and eGFR. Despite impressive numerical performance, key limitations persist: class imbalance in early-stage CKD, restricted generalizability due to single-centre datasets, variability in imaging quality, and the limited interpretability of high-capacity neural networks. As deep learning continues to advance, robust external validation, transparent model explanations, and multi-institutional datasets will be essential to support safe and reliable clinical integration.