Surgical planning of arteriovenous fistulae in routine clinical practice: A machine learning predictive tool.

Journal: The journal of vascular access
PMID: 36765450

Abstract

BACKGROUND: Arteriovenous fistula (AVF) is the preferred vascular access (VA) for hemodialysis, but it is associated with high non-maturation and failure rates. Predicting patient-specific AVF maturation and postoperative changes in blood flow volumes (BFVs) and vessel diameters is of fundamental importance to support the choice of optimal AVF location and improve VA survival. The goal of this study was to employ machine learning (ML) in order to give physicians a fast and easy-to-use tool that provides accurate patient-specific predictions, useful to make AVF surgical planning decisions.

Authors

  • Martina Doneda
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Sofia Poloni
    Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
  • Michela Bozzetto
    Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
  • Andrea Remuzzi
    Department of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), 24044, Italy.
  • Ettore Lanzarone
    Department of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy.

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