International Journal of Reproductive BioMedicine

ISSN: 2476-3772

The latest discoveries in all areas of reproduction and reproductive technology.

 

Artificial intelligence: A novel tool for diagnosing and managing kidney problems in pregnant women

Published date: Jul 29 2025

Journal Title: International Journal of Reproductive BioMedicine

Issue title: International Journal of Reproductive BioMedicine (IJRM): Volume 23, Issue No. 5

Pages: 447 – 448

DOI: 10.18502/ijrm.v23i5.19267

Authors:

Ahmad ShajariDepartment of Medical Sciences, Ali-Ebne-Abitaleb School of Medicine, Islamic Azad University, Yazd

Masoud RostamiDepartment of Languages and Literature, Yazd University, Yazd

Vida Sadat AnooshehAnooshehvida@gmail.comDepartment of Ergonomics, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz

Abstract:

This is a Letter to the Editor and does not have an abstract. Please download the PDF or view the article in HTML.

References:

[1] Gangakhedkar GR, Kulkarni AP. Physiological changes in pregnancy. Indian J Crit Care Med 2021; 25: 189–192.

[2] Gaber TZ, Shemies RS, Baiomy AA, Aladle DA, Mosbah A, Abdel-hady ES, et al. Acute kidney injury during pregnancy and puerperium: An Egyptian hospital-based study. J Nephrol 2021; 34: 1611–1619.

[3] Yadav A, Salas MAP, Coscia L, Basu A, Rossi AP, Sawinski D, et al. Acute kidney injury during pregnancy in kidney transplant recipients. Clin Transplant 2022; 36: e14668.

[4] Davison JM. Kidney function in pregnant women. Am J Kidney Dis 1987; 9: 248–252.

[5] Webster P, Lightstone L, McKay DB, Josephson MA. Pregnancy in chronic kidney disease and kidney transplantation. Kidney Int 2017; 91: 1047–1056.

[6] Oprescu AM, Miro-Amarante G, García-Díaz L, Beltrán LM, Rey VE, Romero-Ternero M. Artificial intelligence in pregnancy: A scoping review. IEEE Access 2020; 8: 181450.

[7] Islam MN, Mustafina SN, Mahmud T, Khan NI. Machine learning to predict pregnancy outcomes: A systematic review, synthesizing framework and future research agenda. BMC Pregnancy Childbirth 2022; 22: 348.

[8] Kakitapalli Y, Ampolu J, Madasu SD, Sai Kumar M. Detailed review of chronic kidney disease. Kidney Dis 2020; 6: 85–91.

[9] Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Rev 2015; 71: 804–818.

[10] Heseltine-Carp W, Courtman M, Browning D, Kasabe A, Allen M, Streeter A, et al. Machine learning to predict stroke risk from routine hospital data: A systematic review. Int J Med Informat 2025; 196: 105811.