International Journal of Scientific Research in Dental and Medical Sciences

International Journal of Scientific Research in Dental and Medical Sciences

Effect of Using Artificial Intelligence in the Prediction and Initial Assessment of Chronic Kidney Disease: A Systematic Review and Meta-analysis

Document Type : Review Article

Authors
Seyed Mehdi Marashi 1
Parya Amini 2
Maedeh Eslamdost 3
1 School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
2 School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
3 School of Medicine, Russian State Social University, Moscow, Russia
10.30485/ijsrdms.2025.509070.1643
Abstract
Background and aim: The present research aims to evaluate the effect of using artificial intelligence in the prediction and initial assessment of chronic kidney disease.
Material and methods: In the present study, the researchers searched the international databases Cochrane, Embase, and MEDLINE (PubMed and Ovid) for keywords related to the study objectives. Considering that recent data is significant with the advancement of artificial intelligence, the search was limited to the last five years, between January 2019 and January 2025. The effect size was used with the random-effects model and REML methods of 95% confidence intervals (CI). Meta-analysis was performed using Stata (as of version 17).
Results: The Area Under the Curve and accuracy of artificial intelligence in the prediction and initial assessment of chronic kidney disease was 90% (ES 0.90 95% CI; 0.28, 1.52) and   87% (ES 0.87 95% CI; 0.25, 1.49), respectively.
Conclusions: Based on the meta-analysis of the present study, artificial intelligence models could be highly effective in the early ancillary diagnosis of chronic kidney disease.
Keywords
Artificial Intelligence
Chronic Kidney Disease
Data Accuracy

Subjects

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International Journal of Scientific Research in Dental and Medical Sciences
Volume 7, Issue 1
Winter 2025
Pages 20-28

  • Receive Date 28 January 2025
  • Revise Date 22 February 2025
  • Accept Date 28 February 2025
  • Publish Date 01 March 2025