L.I. MAZUR, G.A. MAKOVETSKAYA, M.V. KURSHINA

 Samara State Medical University, Samara

Contact details:

Kurshina M.V. — PhD (Medicine), Assistant Lecturer of the Department of Hospital Pediatrics

Address: 165A Karl Marx Ave., 443079 Samara, Russian Federation, tel.: + 7-927-653-50-23, e-mail: marina_dmitriewa@mail.ru

The integration of artificial intelligence (AI) technologies marks a paradigm shift in pediatric nephrology, transforming conventional approaches to the diagnosis, monitoring, and management of renal diseases in children. Literature analysis identified preclinical pathology detection as a key advantage of AI, which enables treatment optimization and a strategic shift from managing to preventing complications. However, the large-scale implementation of digital solutions in pediatrics remains constrained by a paucity of representative data, lack of standardized digitalization protocols, and stringent ethical and legal frameworks.

The purpose of this review is to analyze the current state and prospects of AI integration into pediatric nephrological practice. The paper systematizes data on the diagnostic efficacy of digital technologies and evaluates their potential for the early detection of congenital anomalies of the kidney and urinary tract (CAKUT) and risk prediction for acute (AKI) and chronic kidney injury (CKI). Particular emphasis is placed on the AI capacity for multimodal data synthesis, including genetic profiling and specific biomarkers, which facilitates the transition from standardized protocols toward personalized and predictive medical strategies.

Key words: artificial intelligence, pediatric nephrology, kidney disease diagnostics, personalized medicine, medical image analysis, deep learning, machine learning, onconephrology, congenital kidney anomalies, chronic kidney disease, acute kidney injury, cybersecurity

REFERENCES

1. Nada A., Ahmed Y., Hu J. et al. The American Society of Pediatric Nephrology Quality Improvement and Artificial Intelligence (ASPN QI/AI) Interest Group. AI-powered insights in pediatric nephrology: current applications and future opportunities. Pediatr. Nephrol, 2026, vol. 41 (5), rr. 1275–1286. DOI: 10.1007/s00467-025-06911-1
2. Thadani S., Horvat C.M., Silos C. et al. Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology. Pediatr. Nephrol, 2025. DOI: 10.1007/s00467-025-07019-2
3. Sedashkina O.A. Artificial Intelligence — a New Word in Nephrology: Application Points and Prospects (Literature Review). Menedzher zdravookhraneniya, 2025, no. 1, pp. 76–84 (in Russ.). DOI: 10.21045/1811-0185-2025-1-76-84
4. Kulikova M.Kh. Primeneniye iskusstvennogo intellekta v protsesse diagnostiki zabolevaniy [Application of Artificial Intelligence in Disease Diagnosis]. Digital Era: Materialy II Vserossiyskoy nauchno-prakticheskoy konferentsii, Groznyy, 25 marta 2022 g. Groznyy: Chechenskiy gos. un-t im. A.A. Kadyrova, 2022. Pp. 66–70. DOI: 10.36684/59-2022-2-66-70
5. Terekhov M.R. Possibilities and Prospects of Using Artificial Intelligence in Healthcare. Fenomenus, 2024, no. 1 (27), pp. 165–169 (in Russ.).
6. Yelfimov D.A., Yelfimova I.V., Dolgova I.G. et al. Application of information technologies in practical healthcare. Meditsinskaya nauka i obrazovaniye Urala, 2019, vol. 20, no. 1 (97), pp. 129–132 (in Russ.).
7. Zhao D., Wang W., Tang T. et al. Current progress in artificial intelligence-based medical image analysis for chronic kidney disease: a literature review. Comput. Struct. Biotechnol. J, 2023, vol. 21, rr. 3315–3326.
8. Tomašev N., Glorot X., Rae J.W. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 2019, vol. 572 (7767), rr. 116–119. DOI: 10.1038/s41586-019-1390-1
9. Tsai M.C., Lu H.H., Chang Y.C. et al. Automatic screening of pediatric renal ultrasound abnormalities: deep learning and transfer learning approach. JMIR Med. Inform, 2022, vol. 10 (11), p. e40878. DOI: 10.2196/40878
10. Kuo C.C., Chang C.M., Liu K.T. et al. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit. Med, 2019, vol. 2, r. 29. DOI: 10.1038/s41746-019-0104-2
11. Sedashkina O.A., Kolsanov A.V. Development of an intelligent system for supporting medical decision-making to predict chronic kidney disease in children. Menedzher zdravookhraneniya, 2024, no. 9, pp. 92–102 (in Russ.). DOI: 10.21045/1811-0185-2024-9-92-102
12. Miguel O.X., Kaczmarek E., Lee I. et al. Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis. Sci. Rep, 2024, vol. 14 (1), r. 9013. DOI: 10.1038/s41598-024-59248-4
13. Song S.H., Han J.H., Kim K.S. et al. Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio. Investig. Clin. Urol, 2022, vol. 63 (4), rr. 455–463. DOI: 10.4111/icu.20220085
14. Yin S., Peng Q., Li H. et al. Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children. Urology, 2020, vol. 142, rr. 183–189. DOI: 10.1016/j.urology.2020.05.019
15. Feng C., Ong K., Young D.M. et al. Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis. Bioinformatics, 2024, vol. 40 (1). btad740. DOI: 10.1093/bioinformatics/btad740
16. Van der Kamp A., de Bel T., van Alst L. et al. Automated deep learning-based classification of Wilms tumor histopathology. Cancers (Basel), 2023, vol. 15 (9), rr. 2656. DOI: 10.3390/cancers15092656
17. Testa F., Fontana F., Pollastri F. et al. Automated prediction of kidney failure in IgA nephropathy with deep learning from biopsy images. Clin. J. Am. Soc. Nephrol, 2022, vol. 17 (9), rr. 1316–1324. DOI: 10.2215/CJN.01760222
18. Leventoglu E., Soran M. Clinical characteristics of children with acute post-streptococcal glomerulonephritis and re-evaluation of patients with artificial intelligence. Medeni Med. J, 2024, vol. 39 (3), rr. 221–229. DOI: 10.4274/MMJ.galenos.2024.09382
19. Xu L., Jiang S., Li C. et al. Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach. Ren. Fail, 2024, vol. 46 (2). 2438858. DOI: 10.1080/0886022X.2024.2438858
20. Fragasso T., Raggi V., Passaro D. et al. Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit. J. Anesth. Analg. Crit. Care, 2023, vol. 3 (1), r. 37. DOI: 10.1186/s44158-023-00125-3
21. Dong J., Feng T., Thapa-Chhetry B. et al. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care. Crit. Care, 2021, vol. 25 (1), r. 288. DOI: 10.1186/s13054-021-03724-0