Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.

Journal: Annals of vascular surgery
PMID: 38582202

Abstract

Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms have been applied to PAD.

Authors

  • Jee Hoon Song
    Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA.
  • Roger T Tomihama
    Department of Radiology, Section of Vascular and Interventional Radiology, Linda University School of Medicine, 11234 Anderson Street, Suite MC-2605E, Loma Linda, CA 92354. Electronic address: roger.tomihama@gmail.com.
  • Daniel Roh
    Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA.
  • Andrew Cabrera
    School of Medicine, Loma Linda University, Loma Linda, CA 92354, USA.
  • Alan Dardik
    Department of Surgery, Yale School of Medicine, 10 Amistad Street, Room 437, New Haven, CT 06519; The Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT; Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT. Electronic address: alan.dardik@yale.edu.
  • Sharon C Kiang
    Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, Loma Linda, CA; Department of Surgery, Division of Vascular Surgery, Veterans Affairs Loma Linda Healthcare System, Loma Linda, CA.

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