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Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.
Song, Jee Hoon; Tomihama, Roger T; Roh, Daniel; Cabrera, Andrew; Dardik, Alan; Kiang, Sharon C.
Affiliation
  • Song JH; Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA.
  • Tomihama RT; Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA.
  • Roh D; Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA.
  • Cabrera A; Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA.
  • Dardik A; Division of Vascular Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT.
  • Kiang SC; Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA; Division of Vascular Surgery, Department of Surgery, VA Loma Linda Healthcare System, Loma Linda, CA. Electronic address: sharon.kiang@va.gov.
Ann Vasc Surg ; 2024 Apr 04.
Article in En | MEDLINE | ID: mdl-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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Ann Vasc Surg Journal subject: ANGIOLOGIA Year: 2024 Document type: Article Affiliation country:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Ann Vasc Surg Journal subject: ANGIOLOGIA Year: 2024 Document type: Article Affiliation country: