Nursing Management of Arteriovenous Fistula and Artificial Intelligence: A Scoping Review.

Gaetano Ferrara, Salvatore Angileri, Sara Morales Palomares, Mauro Parozzi, Domenica Gazineo, Silvia Cappelletti, Giovanni Cangelosi, Marco Sguanci, Alberto Paderno, Stefano Mancin, Lea Godino

Journal: Journal of renal care 2026;52(1):e70056

PMID: 41823318

Abstract

INTRODUCTION

Chronic kidney disease is a progressive condition that often necessitates kidney replacement therapy, with haemodialyses relying on stable, functional arteriovenous fistulas. While arteriovenous fistulas offer several benefits, they are susceptible to complications that present considerable management challenges.

OBJECTIVE

This review aimed to investigate the potential role of artificial intelligence in the management of arteriovenous fistulas.

DESIGN

Scoping review was reported following the PRISMA-Scoping eview guidelines and Joanna Briggs Institute methodology.

PARTICIPANTS

Adults with chronic kidney disease.

MEASUREMENTS

Relevant studies on artificial intelligence applications in arteriovenous fistulas management were identified through comprehensive searches in PubMed, Embase, Cochrane Library, and CINAHL, supplemented by grey literature. Data extraction and synthesis employed standardised methods to categorise artificial intelligence techniques and their clinical applications.

RESULTS

Twenty-three studies were included, exploring artificial intelligence applications in arteriovenous fistulas management, including prevention, failure management, and early stenosis detection in haemodialyses patients. In this context, artificial intelligence refers to computer systems that learn from clinical data to identify patterns and assist clinicians in recognising complications earlier. Convolutional Neural Networks effectively detected arteriovenous fistulas stenosis through non-invasive acoustic analysis, while combining Convolutional Neural Networks with Bi-directional Long Short-Term Memory networks improved stenosis severity classification. The Extreme Gradient Boosting and machine learning techniques, such as decision trees and Support Vector Machines, demonstrated strong predictive capabilities for arteriovenous fistulas failure and maturation.

CONCLUSIONS

Artificial intelligence has significant potential to revolutionise arteriovenous fistulas management, enabling proactive monitoring, reducing complications, and enhancing personalised care. These findings also suggest relevant implications for nursing practice, particularly in supporting fistula assessment and ongoing surveillance in haemodialyses patients.

PROTOCOL REGISTRATION

The protocol was registered in the Open Science Framework database (DOI: doi.org/10.17605/OSF.IO/5AVWS).

© 2026 European Dialysis and Transplant Nurses Association/European Renal Care Association.

Address: Department of Nefrology and Dialysis Unit, Ramazzini Hospital, Carpi, Modena, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Research Unit, Clinical and Cellular Pathophysiology, Meyer Children's Hospital IRCCS, Florence, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Department of Pharmacy, Health and Nutritional Sciences (DFSSN), University of Calabria, Rende, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; School of Nursing, "San Paolo" Campus, Asst Santi Paolo e Carlo, University of Milan, Milan, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Governo Clinico e Qualità, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; UOC Nefrologia e Dialisi, ASST LARIANA, San Fermo della Battaglia, Como, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Units of Diabetology, ASUR, Fermo, Marche, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Department of Medicine and Surgery, Research Unit of Nursing Science, Università Campus Bio-Medico di Roma, Rome, Italy.; IRCCS Humanitas Research Hospital, Milan, Italy.; Department of Biomedical Sciences, Humanitas University, Milan, Italy.; Society of Nurses in the Nephrology Area (SIAN), Olbia, Italy.; Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
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