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Artificial intelligence-based predictive models in vascular diseases

delete2023-09-01
delete12
PRE
AI
F
Fabien Lareyre
A
Arindam Chaudhuri
C
Christian‐Alexander Behrendt
A
Alexandre Pouhin
M
Martin Teraa
J
Jonathan R. Boyle
R
Riikka Tulamo
J
Juliette Raffort *
DOI:10.1053/j.semvascsurg.2023.05.002delete
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摘要

摘要

En 中文
Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence-based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence-based predictive models in clinical practice are discussed.(c) 2023 Elsevier Inc. All rights reserved.
Keyword:
Artificial intelligence
Machine learning
Predictive model
Vascular disease
Aortic disease
Peripheral artery disease
Lower extremity arterial disease
Carotid stenosis

期刊

Journal of Vascular Surgery 封面图
Journal of Vascular Surgery
IF:
3.6
论文数:
2.2W
被引数:
3.1W

机构

U
university of helsinki
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4.1W
论文数: 3.6W
被引数: 51
U
Universite Bourgogne Europe
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4.3K
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被引数: 3
U
Utrecht University Medical Center
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2.1W
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被引数: 30
I
institut national de la sante et de la recherche medicale (inserm)
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U
University of Cambridge
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7.7W
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被引数: 13.7W
C
CHU Dijon Bourgogne
学者数:
3.6K
论文数: 2.5K
被引数: 1.7K
H
helsinki university central hospital
学者数:
7.8K
论文数: 6.8K
被引数: 10
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