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Föllmer, B.* ; Williams, M.C.* ; Dey, D.* ; Arbab-Zadeh, A.* ; Maurovich-Horvat, P.* ; Volleberg, R.H.J.A.* ; Rueckert, D.* ; Schnabel, J.A. ; Newby, D.E.* ; Dweck, M.R.* ; Guagliumi, G.* ; Falk, V.* ; Vázquez Mézquita, A.J.* ; Biavati, F.* ; Isgum, I.* ; Dewey, M.*

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

Nat. Rev. Cardiol. 21, 51-64 (2024)
Verlagsversion DOI PMC
Closed
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Computed-tomography Angiography; Optical Coherence Tomography; Fractional Flow Reserve; Thin-cap Fibroatheroma; Ct Angiography; Intravascular Ultrasound; Adverse Outcomes; Chest-pain; Cardiac Ct; Machine
Sprache englisch
Veröffentlichungsjahr 2024
Prepublished im Jahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1759-5002
e-ISSN 1759-5010
Zeitschrift Nature reviews. Cardiology
Quellenangaben Band: 21, Heft: 1, Seiten: 51-64 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort Heidelberger Platz 3, Berlin, 14197, Germany
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen DFG Priority Programme Radiomics
graduate programme on quantitative biomedical imaging (BIOQIC
German Research Foundation in the Heisenberg Programme
FP7 Programme of the European Commission
Sir Jules Thorn Award for Biomedical Research
Medical Research Council
Chief Scientist Office
German Research Foundation
Wellcome Trust
Royal Society
Engineering and Physical Sciences Research Council and Innovate UK
EU
Deutsche Forschungsgemeinschaft
Bundesministerium fuer Bildung und Forschung
ERC Advanced Grant Deep4MI
NIH/NHLBI
British Heart Foundation
Chest Heart Stroke Scotland
GUIDE-IT project on data sharing of medical imaging trials
Digital Health Accelerator of the Berlin Institute of Health
Berlin University Alliance
Quantitative Cardiovascular Imaging meeting
DOI 10.1038/s41569-023-00900-3
WOS ID 001031454800001
Scopus ID 85165010377
PubMed ID 37464183
Erfassungsdatum 2023-10-06
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