SynthesisRenal failure2024
Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.
Synthesis in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of artificial intelligence in chronic kidney disease: a systematic review.Revista de saude publica · 2026Pooled it
- Prediction models for progression from diabetic kidney disease to end-stage renal disease: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Unraveling the Impact of Serum Zinc Levels on Chronic Kidney Disease: Machine Learning and SHAP Value Interpretation.Food science & nutrition · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Quantum-enhanced multimodal prognostic transformer for skin disease progression prediction and visualization.Scientific reports · 2026Article
- Development and Validation of Machine Learning Models for Predicting 7-Day Mortality in Critically Ill Patients with Traumatic Spinal Cord Injury: A Multicenter Retrospective Study.Neurocritical care · 2026Article
- A Cross-Ethnicity Validated Machine Learning Model for the Progression of Chronic Kidney Disease in Individuals over 50 Years Old.Journal of clinical medicine · 2026Article
- Multi-omics and machine learning identify FN1 and ALDH2 as diagnostic biomarkers and therapeutic targets in early and late diabetic kidney disease.Renal failure · 2025Article
- Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence in chronic kidney disease: Bibliometric and visual analysis of trends and future directions.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD.
methodSearch was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST).
resultsA total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41-0.44,
conclusionsThis study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.