Artificial intelligence-based predictive models in vascular diseases.
Aortic disease
Artificial intelligence
Carotid stenosis
Lower extremity arterial disease
Machine learning
Peripheral artery disease
Predictive model
Vascular disease
Journal
Seminars in vascular surgery
ISSN: 1558-4518
Titre abrégé: Semin Vasc Surg
Pays: United States
ID NLM: 8809602
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
02
12
2022
revised:
24
04
2023
accepted:
24
05
2023
medline:
31
10
2023
pubmed:
21
10
2023
entrez:
20
10
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37863618
pii: S0895-7967(23)00038-8
doi: 10.1053/j.semvascsurg.2023.05.002
pii:
doi:
Substances chimiques
Cardiovascular Agents
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
440-447Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare no competing interest