Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes
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- Alyssa M. Flores
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA.
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- Falen Demsas
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA.
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- Nicholas J. Leeper
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA.
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- Elsie Gyang Ross
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA.
抄録
<jats:p>Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.</jats:p>
収録刊行物
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- Circulation Research
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Circulation Research 128 (12), 1833-1850, 2021-06-11
Ovid Technologies (Wolters Kluwer Health)