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

Nature Reviews Cardiology volume 21, pages 51–64 (2024)Cite this article

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Abstract

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.

Key points

  • Artificial intelligence (AI) might have the potential to transform the assessment of vulnerable or high-risk plaque in coronary arteries by improving the detection, quantification and prognostication of vulnerable plaque and integration with other imaging and clinical parameters.

  • The advantages of AI for the assessment of vulnerable plaque images include reducing observer variability, improving accuracy, enabling standardization, improving speed and facilitating the synthesis of diverse information.

  • The challenges for the development and implementation of AI include the presence of anatomical variations and imaging artefacts; the lack of reproducibility, generalizability and robustness across diverse imaging platforms; and the potential for the technology to introduce or worsen biases.

  • Clinical research has already been performed on AI tools for plaque assessment, but validated commercial solutions for clinical use are not yet available.

  • For AI to achieve its true potential for vulnerable plaque assessment in clinical practice, large and diverse studies are required, and AI tools must be trustworthy, explainable and interpretable.

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Fig. 1: Basics of artificial intelligence, machine learning and deep learning.
Fig. 2: The concept of vulnerable plaques and high-risk plaque features in CCTA, IVUS and OCT images.
Fig. 3: The interaction between tasks supported by AI tools for the assessment of vulnerable plaques in coronary arteries.
Fig. 4: Roadmap for AI in the imaging of vulnerable plaques.

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Acknowledgements

We thank the German Research Foundation (grant number DE 1361/22-1) for funding the second Quantitative Cardiovascular Imaging meeting. M.C.W. is supported by the British Heart Foundation (FS/ICRF/20/26002). D.D. has received software royalties from Cedars–Sinai Medical Center and grant support from NIH/NHLBI. D.R. is supported by the ERC Advanced Grant Deep4MI, as well as by grants from the British Heart Foundation, Bundesministerium für Bildung und Forschung, Deutsche Forschungsgemeinschaft, EU Horizon 2020, Engineering and Physical Sciences Research Council and InnovateUK. He is a recipient of the Alexander Humboldt Professorship for AI. J.A.S. is supported by a Helmholtz Distinguished Professorship and a TUM Liesel Beckmann Professorship, as well as by grants from the British Heart Foundation, Bundesministerium für Gesundheit, Cancer Research UK, Engineering and Physical Sciences Research Council, InnovateUK, The Royal Society and The Wellcome Trust. D.E.N. has received research funding from the British Heart Foundation, Chest Heart Stroke Scotland, Chief Scientist Office, Medical Research Council and The Wellcome Trust. M.R.D. is supported by the British Heart Foundation (FS/SCRF/21/32010) and is the recipient of the Sir Jules Thorn Award for Biomedical Research 2015 (15/JTA). M.D. has received grant support from the FP7 Programme of the European Commission for the DISCHARGE trial (EC-GA 603266 in HEALTH.2013.2.4.2-2), and has also received grant support from the German Research Foundation in the Heisenberg Programme (DE 1361/14-1, DFG project 213705389), the graduate programme on quantitative biomedical imaging (BIOQIC, GRK 2260/1, DFG project 289347353) and for fractal analysis of myocardial perfusion (DE 1361/18-1, DFG project 392304398), the DFG Priority Programme Radiomics (DFG project 402688427) for the investigation of coronary plaque and coronary flow (DE 1361/19-1 (DFG project 428222922) and DE 1361/20-1 (DFG project 428223139) in SPP 2177/1), the GUIDE-IT project on data sharing of medical imaging trials (DE 1361/24-1 (DFG project 495697118)), the Quantitative Cardiovascular Imaging meeting (DE 1361/22-1) and the Future of Medical Imaging meeting (DE 1361/28-1). He has also received funding from the Berlin University Alliance (GC_SC_PC 27) and from the Digital Health Accelerator of the Berlin Institute of Health.

Author information

Author notes

  1. These authors contributed equally: Bernhard Föllmer, Michelle C. Williams.

  2. These authors jointly supervised this work: Ivana Išgum, Marc Dewey.

Authors and Affiliations

  1. Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Bernhard Föllmer, Aldo J. Vázquez Mézquita, Federico Biavati & Marc Dewey

  2. Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

    Michelle C. Williams, David E. Newby & Marc R. Dweck

  3. Biomedical Imaging Research Institute and Department of Imaging, Medicine and Biomedical Sciences, Cedars–Sinai Medical Center, Los Angeles, CA, USA

    Damini Dey

  4. Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Armin Arbab-Zadeh

  5. Department of Radiology, Medical Imaging Center, Semmelweis University, Budapest, Hungary

    Pál Maurovich-Horvat

  6. Department of Cardiology, Radboud University Medical Center, Nijmegen, Netherlands

    Rick H. J. A. Volleberg

  7. Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany

    Daniel Rueckert

  8. Department of Computing, Imperial College London, London, UK

    Daniel Rueckert

  9. School of Biomedical Imaging and Imaging Sciences, King’s College London, London, UK

    Julia A. Schnabel

  10. Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany

    Julia A. Schnabel

  11. School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

    Julia A. Schnabel

  12. Division of Cardiology, IRCCS Galeazzi Sant’Ambrogio Hospital, Milan, Italy

    Giulio Guagliumi

  13. Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité, Charité Universitätsmedizin, Berlin, Germany

    Volkmar Falk

  14. Department of Health Science and Technology, ETH Zurich, Zurich, Switzerland

    Volkmar Falk

  15. Berlin Institute of Health at Charité and DZHK (German Centre for Cardiovascular Research), Partner Site, Berlin, Germany

    Volkmar Falk

  16. Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands

    Ivana Išgum

  17. Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands

    Ivana Išgum

  18. Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands

    Ivana Išgum

  19. Berlin Institute of Health, Campus Charité Mitte, Berlin, Germany

    Marc Dewey

  20. DZHK (German Centre for Cardiovascular Research), Partner Site Berlin and Deutsches Herzzentrum der Charité (DHZC), Charité – Universitätsmedizin Berlin, Berlin, Germany

    Marc Dewey

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  1. Bernhard Föllmer
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Contributions

B.F. and M.D. researched data for the article. B.F., M.C.W. and M.D. contributed to the discussion of content. B.F., M.C.W., D.D., A.A.-Z., P.M.-H., R.H.J.A.V., D.R. J.A.S., D.E.N., M.R.D., G.G., V.F., A.J.V.M., F.B., I.I. and M.D. wrote the manuscript. All authors contributed to reviewing and editing the manuscript before submission.

Corresponding authors

Correspondence to Bernhard Föllmer or Marc Dewey.

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Competing interests

M.C.W. has given talks for Canon Medical Systems, Novartis and Siemens Healthineers. A.A.-Z. has received research support from Canon Medical Systems. P.M.-H. is a shareholder of Neumann Medical. D.R. has received consultancy fees from Heartflow and IXICO. D.E.N. receives grants, acts as a consultant and has clinical trial contracts with Abbott, Amgen, AstraZeneca, Autoplaque, BMS, Boehringer Ingelheim, Eli Lilly, GE HealthCare, GSK, Janssen, Life Molecular Imaging, MSD, Novartis, Pfizer, Philips, Roche, Sanofi, Siemens, Silence, SOFIE, Toshiba, UCB, Vifor, Wyeth and Zealand. He collaborates with the publications chair from the BMJ Group and Elsevier. He is the chief investigator of the SCOT-HEART and PRE18FFIR trials. M.R.D. has received speaker fees from Edwards, Novartis and Pfizer and consultancy fees from Beren, Jupiter Bioventures, Novartis and Silence Therapeutics. G.G. has a consultant agreement with Abbott Vascular, Gentuity, Infraredx and Panovision, and has received a research grant in the past 36 months from Abbott Vascular, Amgen and Infraredx. V.F. has received educational grants, fees for lectures and speeches, fees for professional consultation, as well as research and study funds from Abbott, Abiomed, Berlin Heart, Biotronik, Boston Scientific, Edwards Lifesciences, JOTEC/CryoLife, LivaNova, Medtronic, Novartis and Zurich Heart. I.I. has received institutional research grants by Esaote and Pie Medical Imaging and received an institutional research grant funded by Dutch Technology Foundation with the participation of Pie Medical Imaging and Philips Healthcare. She is also a co-inventor on several patents (US 10,176,575 B2; US 10,395,366 B2; US 11,004,198 B2; US 10,699,407 B2) and patent applications (17317746, 16911323) on the detection of functionally significant coronary stenosis. M.D. is the publications chair of the European Society of Radiology (ESR; 2022–2025); the opinions expressed in this article are the author’s own and do not represent the view of the ESR. He is also the editor of Cardiac CT (published by Springer Nature) and has institutional master research agreements with Canon, General Electric, Philips and Siemens, the arrangements of which are managed by Charité – Universitätsmedizin Berlin. He also holds a joint approved patent on dynamic perfusion analysis using fractal analysis (EPO 2022 EP3350773A1 and USPTO 2021 10,991,109). The other authors declare no competing interests.

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Nature Reviews Cardiology thanks Carlo de Cecco; Joseph Schoepf, who co-reviewed with Daniel Pinos Sanchez; and Christian Tesche for their contribution to the peer review of this work.

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Föllmer, B., Williams, M.C., Dey, D. et al. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol 21, 51–64 (2024). https://doi.org/10.1038/s41569-023-00900-3

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