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Kampaktsis, P.N.* ; Emfietzoglou, M.* ; Al Shehhi, A.* ; Fasoula, N.-A. ; Bakogiannis, C.* ; Mouselimis, D.* ; Tsarouchas, A.* ; Vassilikos, V.P.* ; Kallmayer, M.* ; Eckstein, H.H.* ; Hadjileontiadis, L.* ; Karlas, A.

Artificial intelligence in atherosclerotic disease: Applications and trends.

Front. Cardiovasc. Med. 9:949454 (2023)
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Open Access Gold
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Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.
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Publication type Article: Journal article
Document type Review
Keywords Artificial Intelligence ; Atherosclerosis ; Carotid Artery Disease ; Coronary Artery Disease ; Machine Learning ; Peripheral Arterial Disease; Coronary-artery-disease; Fractional Flow Reserve; Ct Angiography; Diagnostic Performance; Clinical Notes; Cardiac Ct; Follow-up; Risk; Classification; Plaque
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 2297-055X
e-ISSN 2297-055X
Journal Frontiers in Cardiovascular Medicine
Quellenangaben Volume: 9, Issue: , Pages: , Article Number: 949454 Supplement: ,
Publisher Frontiers
Publishing Place Lausanne
Reviewing status Peer reviewed
Institute(s) Institute of Biological and Medical Imaging (IBMI)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-505593-001
G-509200-001
Grants Deutsche Gesellschaft fur Gefaesschirurgie und Gefaessmedizin (DGG)
DZHK (German Centre for Cardiovascular Research)
DOI 10.3389/fcvm.2022.949454
WOS ID 000923816000001
Scopus ID 85147300805
PubMed ID 36741834
Erfassungsdatum 2023-02-10
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