AI-Powered Early Detection of Chronic Kidney Disease (CKD): Transforming Nephrology

1. The Importance of Early CKD Detection

Chronic kidney disease (CKD) is a major global health challenge, with most patients diagnosed only after losing more than half of their kidney function. Late detection limits opportunities for early intervention, increasing the likelihood of complications such as kidney failure and cardiovascular disease. To mitigate these risks, early diagnosis and risk stratification are essential for optimizing treatment and improving outcomes.

Artificial intelligence (AI) is emerging as a powerful tool in healthcare, offering innovative solutions for CKD screening and risk assessment. By leveraging AI-driven models, healthcare providers can identify high-risk individuals and implement timely interventions, ultimately improving patient care.

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2. AI-Driven Approaches for Early CKD Detection

Two primary AI methodologies have shown significant promise in CKD detection:

(1) Deep Learning with Retinal Imaging

Since the retina and kidneys share similar vascular structures, retinal images can offer valuable insights into kidney health. AI models utilizing this imaging technique have demonstrated strong predictive capabilities:

• RetiKid: Developed in Singapore, this AI model analyzes retinal images to detect CKD. It achieved accuracies of 73% and 84% in independent validation datasets from Singapore and China, respectively.
• RetiKid-Diab: Designed for diabetic patients, this model assesses CKD risk based on retinal images from individuals with diabetes. It achieved validation accuracies of 76% and 73% in two separate Singaporean cohorts.
• Ultrawide-Field Retinal Imaging: A deep-learning model trained on ultrawide-field retinal images from 23 hospitals in China demonstrated an accuracy of 81%. However, the higher cost and limited availability of this technology may restrict its widespread adoption.

One major advantage of retinal imaging for CKD detection is its non-invasive nature. Since retinal imaging is already widely used for diabetic retinopathy screening, integrating it into CKD screening programs could enhance early detection efforts, particularly in community and primary care settings.

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(2) Machine Learning with Laboratory Data

Since CKD diagnosis and progression are primarily based on laboratory tests, AI-driven models utilizing clinical data provide another effective approach for early detection:

• Klinrisk Model: Developed in Canada, this random forest-based AI model was validated on data from over 5.8 million individuals. It outperformed the traditional KDIGO (Kidney Disease Improving Global Outcomes) framework in predicting CKD progression.
• KidneyIntelX Model: This U.S.-developed model predicts CKD progression risk using a combination of proprietary biomarkers and clinical data. It has received FDA approval and is currently being implemented in clinical practice, demonstrating improved predictive accuracy and adherence to treatment guidelines.

Laboratory-based AI models offer a cost-effective and scalable solution, as they leverage routinely collected clinical data. Additionally, these models can be integrated with clinical decision support systems to enhance CKD management.

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3. Selecting the Right AI Model for CKD Detection

The choice of AI model depends on healthcare infrastructure, regulatory considerations, and resource availability:

• Retinal imaging-based models are ideal for non-invasive screening but require access to specialized imaging equipment.
• Laboratory data-based models are highly scalable and practical in settings where routine clinical testing is already established.
• Biomarker-based models (e.g., KidneyIntelX) offer high accuracy but require specialized assays and fresh biological samples, limiting their accessibility in certain regions.

Retinal imaging approaches are well-suited for early screening, whereas laboratory-based models are more effective for risk assessment and treatment planning. Healthcare systems should consider these factors when integrating AI into CKD detection and management.

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4. Challenges and Future Directions

Despite AI’s potential to revolutionize CKD detection, several challenges must be addressed:

• Regulatory and reimbursement frameworks: Clear policies are needed to support the clinical adoption of AI-based diagnostic tools.
• Data sharing and global collaboration: AI models require extensive external validation across diverse populations, necessitating cross-border data-sharing agreements.
• Clinician education and AI integration: Healthcare providers must receive adequate training to effectively incorporate AI into clinical workflows.

For AI to become a standard component of CKD management, researchers, clinicians, policymakers, and healthcare technology companies must work together to bridge the gap between innovation and real-world application.

By 2030, AI-powered CKD screening and risk assessment could become routine in clinical practice, significantly reducing disease burden and enhancing patient outcomes. The ultimate goal is not only early detection but also proactive intervention to preserve kidney function and improve quality of life for millions worldwide.

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References

1. Tangri, N., & Sabanayagam, C. (2025). Artificial intelligence approaches to enable early detection of CKD. Nature Reviews Nephrology, 21, 153–154. https://doi.org/10.1038/s41581-025-00933-6
2. Sabanayagam, C., et al. (2020). A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. The Lancet Digital Health, 2(6), e295–e302.
3. Tangri, N., et al. (2024). Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS program and CREDENCE trial. Diabetes, Obesity and Metabolism, 26, 3371–3380.
4. Nadkarni, G. N., et al. (2023). Derivation and independent validation of KidneyIntelX.dkd: a prognostic test for the assessment of diabetic kidney disease progression. Diabetes, Obesity and Metabolism, 25, 3779–3787.