Artificial intelligence in vascular surgical decision making.

Journal: Seminars in vascular surgery
Published Date:

Abstract

Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges.

Authors

  • Fabien Lareyre
    Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France.
  • Kak Khee Yeung
    Department of Surgery, Amsterdam UMC location Vrije Universiteit and Location University of Amsterdam, Amsterdam Cardiovascular Sciences, Atherosclerosis & Ischemic Syndromes 26066 De Boelelaan 1117, 1081HVAmsterdam The Netherlands.
  • Lisa Guzzi
    Institute 3IA Côte d'Azur, Université Côte d'Azur, Côte d'Azur, France; Epione Team, Inria, Université Côte d'Azur, Sophia Antipolis, France.
  • Gilles Di Lorenzo
    Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France.
  • Arindam Chaudhuri
    Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK.
  • Christian-Alexander Behrendt
    Research Group GermanVasc, Department of Vascular Medicine, University Heart and Vascular Centre UKE Hamburg, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
  • Konstantinos Spanos
    Department of Vascular Surgery, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece. Electronic address: spanos.kon@gmail.com.
  • Juliette Raffort
    Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France. Electronic address: juliette.raffort@hotmail.fr.