Cor Vasa 2021, 63(Suppl. 1):97-106

The year in cardiovascular medicine 2020: digital health and innovation

Charalambos Antoniades1, 2*, Folkert W. Asselbergs3, 4, Panos Vardas5, 6
1 Acute Vascular Imaging Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX39DU, UK;
2 Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, L6 West Wing, John Radcliffe Hospital, Headley Way, Oxford OX39DU, UK;
3 Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Heidelberglaan 8, 3584 CX, Utrecht, the Netherlands;
4 Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, 222 Euston Road, NW1 2DA, London, UK;
5 Heart Sector, Hygeia Hospitals Groups, Erithrou Stavrou 4, Marousi 151 23, Athens, Greece;
6 Cardiology Department, Medical School, University of Crete, University Campus of Voutes, 700 13, Heraclion, Greece

Keywords: Artificial intelligence, Deep learning, Atherosclerosis, Cardiovascular diseases, Machine learning, Digital health, Big Data

Received: June 15, 2021; Accepted: June 15, 2021; Published: July 1, 2021  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Antoniades C, Asselbergs FW, Vardas P. The year in cardiovascular medicine 2020: digital health and innovation. Cor Vasa. 2021;63(Suppl. 1):97-106.

Reproduced from: Antoniades C, Asselbergs FW, Vardas P. The year in cardiovascular medicine 2020: digital health and innovation. Eur Heart J. 2021 Feb 14;42(7):732-739. https://doi.org/10.1093/eurheartj/ehaa1065, by permission of Oxford University Press on behalf of the European Society of Cardiology.

All rights reserved; no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior written permission of the Publishers.

For Permissions, please email: journals.permissions@oup.com

The opinions expressed in the Journal item reproduced as this reprint are those of the authors and contributors, and do not necessarily reflect those of the European Society of Cardiology, the editors, the editorial board, Oxford University Press or the organization to which the authors are affiliated.

The mention of trade names, commercial products or organizations, and the inclusion of advertisements in this reprint do not imply endorsement by the Journal, the editors, the editorial board, Oxford University Press or the organization to which the authors are affiliated. The editors and publishers have taken all reasonable precautions to verify drug names and doses, the results of experimental work and clinical findings published in the Journal. The ultimate responsibility for the use and dosage of drugs mentioned in this reprint and in interpretation of published material lies with the medical practitioner, and the editors and publisher cannot accept liability for damages arising from any error or omissions in the Journal or in this reprint. Please inform the editors of any errors.

Oxford University Press, OPL, and the European Society of Cardiology are not responsible or in any way liable for the accuracy of the reprint, for any errors, omissions, or inaccuracies, or for any consequences arising therefrom. The Czech Society of Cardiology is solely responsible for this reprint.

Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Nicholls M. Machine learning-state of the art. Eur Heart J 2019;40:3668-3669. Go to original source... Go to PubMed...
    2. Lamata P. Teaching cardiovascular medicine to machines. Cardiovascular Research 2018;114:e62-e64. 10.1093/cvr/cvy127 Go to original source... Go to PubMed...
    3. Sekelj S, Sandler B, Johnston E, Pollock KG, Hill NR, Gordon J, Tsang C, Khan S, Ng FS, Farooqui U. Detecting undiagnosed atrial fibrillation in UK primary care: validation of a machine learning prediction algorithm in a retrospective cohort study. Eur J Prev Cardiol 2020. Go to original source... Go to PubMed...
    4. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 2019;394:861-867. Go to original source... Go to PubMed...
    5. Han X, Hu Y, Foschini L, Chinitz L, Jankelson L, Ranganath R. Deep learning models for electrocardiograms are susceptible to adversarial attack. Nat Med 2020;26:360-363. Go to original source... Go to PubMed...
    6. Wagner P, Strodthoff N, Bousseljot RD, Kreiseler D, Lunze FI, Samek W, Schaeffter T. PTB-XL, a large publicly available electrocardiography dataset. Sci Data 2020;7:154. Go to original source... Go to PubMed...
    7. Zheng J, Zhang J, Danioko S, Yao H, Guo H, Rakovski C. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Sci Data 2020;7:48. Go to original source... Go to PubMed...
    8. Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr., Schon TB, Ribeiro ALP. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 2020;11:1760. Go to original source... Go to PubMed...
    9. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L, Hung G, Lee J, Kowey P, Talati N, Nag D, Gummidipundi SE, Beatty A, Hills MT, Desai S, Granger CB, Desai M, Turakhia MP; Apple Heart Study I. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med 2019;381:1909-1917. Go to original source... Go to PubMed...
    10. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, Falk V, Gonzalez-Juanatey JR, Harjola VP, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GMC, Ruilope LM, Ruschitzka F, Rutten FH, van der Meer P; Group ESCSD. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2016;37:2129-2200. Go to original source... Go to PubMed...
    11. Gutman SJ, Costello BT, Papapostolou S, Voskoboinik A, Iles L, Ja J, Hare JL, Ellims A, Kistler PM, Marwick TH, Taylor AJ. Reduction in mortality from implantable cardioverter-defibrillators in non-ischaemic cardiomyopathy patients is dependent on the presence of left ventricular scar. Eur Heart J 2019;40:542-550. Go to original source... Go to PubMed...
    12. Tokodi M, Schwertner WR, Kovács A, Tősér Z, Staub L, Sárkány A, Lakatos BK, Behon A, Boros AM, Perge P, Kutyifa V, Széplaki G, Gellér L, Merkely B, Kosztin A. Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score. Eur Heart J 2020;41:1747-1756. Go to original source... Go to PubMed...
    13. Tokodi M, Schwertner WR, Kosztin A, Merkely B. The ongoing quest for improving machine learning-based risk stratification. Eur Heart J 2020;41:2914-2915. Go to original source... Go to PubMed...
    14. Cikes M, Sanchez-Martinez S, Claggett B, Duchateau N, Piella G, Butakoff C, Pouleur AC, Knappe D, Biering-Sorensen T, Kutyifa V, Moss A, Stein K, Solomon SD, Bijnens B. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail 2019;21:74-85. Go to original source... Go to PubMed...
    15. Kim WH, Kim JT. Machine learning-based mortality prediction: how to be connected to daily clinical practice? Eur Heart J 2020;41:2913-2913. Go to original source... Go to PubMed...
    16. Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019;25:70-74. Go to original source... Go to PubMed...
    17. Commandeur F, Slomka PJ, Goeller M, Chen X, Cadet S, Razipour A, McElhinney P, Gransar H, Cantu S, Miller RJH, Rozanski A, Achenbach S, Tamarappoo BK, Berman DS, Dey D. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovasc Res 2019. Go to original source...
    18. Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: a radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2020. Go to original source... Go to PubMed...
    19. Collet JP, Thiele H, Barbato E, Barthelemy O, Bauersachs J, Bhatt DL, Dendale P, Dorobantu M, Edvardsen T, Folliguet T, Gale CP, Gilard M, Jobs A, Juni P, Lambrinou E, Lewis BS, Mehilli J, Meliga E, Merkely B, Mueller C, Roffi M, Rutten FH, Sibbing D, Siontis GCM; Group ESCSD. 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J 2020. Go to original source... Go to PubMed...
    20. Taylor A, Yang E. Comparing American and European guidelines for the initial diagnosis of stable ischaemic heart disease. Eur Heart J 2020;41:811-815. Go to original source... Go to PubMed...
    21. Al'Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z, Alawamlh OAH, Lee B, Pandey M, Achenbach S, Al-Mallah MH, Andreini D, Bax JJ, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJW, Cury RC, DeLago A, Feuchtner G, Hadamitzky M, Hausleiter J, Kaufmann PA, Kim YJ, Leipsic JA, Maffei E, Marques H, Goncalves PA, Pontone G, Raff GL, Rubinshtein R, Villines TC, Gransar H, Lu Y, Jones EC, Pena JM, Lin FY, Min JK, Shaw LJ. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J 2020;41:359-367. Go to original source... Go to PubMed...
    22. Sengupta PP, Shrestha S, Zeb I. Solving coronary risk: time to feed machines some calcium (score) supplements. Eur Heart J 2020;41:368-370. Go to original source... Go to PubMed...
    23. Pennell D, Delgado V, Knuuti J, Maurovich-Horvat P, Bax JJ. The year in cardiology: imaging. Eur Heart J 2020;41:739-747. Go to original source... Go to PubMed...
    24. Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L, Margaritis M, Shirodaria C, Kampoli AM, Akoumianakis I, Petrou M, Sayeed R, Krasopoulos G, Psarros C, Ciccone P, Brophy CM, Digby J, Kelion A, Uberoi R, Anthony S, Alexopoulos N, Tousoulis D, Achenbach S, Neubauer S, Channon KM, Antoniades C. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med 2017;9(398):eaal2658. Go to original source... Go to PubMed...
    25. Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Hutt Centeno E, Thomas S, Herdman L, Kotanidis CP, Thomas KE, Griffin BP, Flamm SD, Antonopoulos AS, Shirodaria C, Sabharwal N, Deanfield J, Neubauer S, Hopewell JC, Channon KM, Achenbach S, Antoniades C. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet 2018;392:929-939. Go to original source... Go to PubMed...
    26. Oikonomou EK, Desai MY, Marwan M, Kotanidis CP, Antonopoulos AS, Schottlander D, Channon KM, Neubauer S, Achenbach S, Antoniades C. Perivascular fat attenuation index stratifies cardiac risk associated with high-risk plaques in the CRISP-CT study. J Am Coll Cardiol 2020;76:755-757. Go to original source... Go to PubMed...
    27. Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J 2019;40:3529-3543. Go to original source... Go to PubMed...
    28. Investigators S-H, Newby DE, Adamson PD, Berry C, Boon NA, Dweck MR, Flather M, Forbes J, Hunter A, Lewis S, MacLean S, Mills NL, Norrie J, Roditi G, Shah ASV, Timmis AD, van Beek EJR, Williams MC. Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med 2018;379:924-933. Go to original source... Go to PubMed...
    29. Bartelt A, Leipsic J, Weber C. The new age of radiomic risk profiling: perivascular fat at the heart of the matter. Eur Heart J 2019;40:3544-3546. Go to original source... Go to PubMed...
    30. Lin A Kolossváry M Yuvaraj J Cadet S Mcelhinney P A Jiang C Nerlekar N Nicholls S J Slomka P J Maurovich-Horvat P Wong D T Dey D. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype. JACC: Cardiovascular Imaging 2020;13:2371-2383. 10.1016/j.jcmg.2020.06.033 Go to original source... Go to PubMed...
    31. Antoniades C, Antonopoulos AS, Deanfield J. Imaging residual inflammatory cardiovascular risk. Eur Heart J 2020;41:748-758. Go to original source... Go to PubMed...
    32. Leeson P, Fletcher AJ. Combining artificial intelligence with human insight to automate echocardiography. Circ Cardiovasc Imaging 2019;12:e009727. Go to original source... Go to PubMed...
    33. Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, Heidenreich PA, Harrington RA, Liang DH, Ashley EA, Zou JY. Video-based AI for beat-to-beat assessment of cardiac function. Nature 2020;580:252-256. Go to original source... Go to PubMed...
    34. Littlejohns TJ, Holliday J, Gibson LM, Garratt S, Oesingmann N, Alfaro-Almagro F, Bell JD, Boultwood C, Collins R, Conroy MC, Crabtree N, Doherty N, Frangi AF, Harvey NC, Leeson P, Miller KL, Neubauer S, Petersen SE, Sellors J, Sheard S, Smith SM, Sudlow CLM, Matthews PM, Allen NE. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun 2020;11:2624. Go to original source... Go to PubMed...
    35. Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK, Neubauer S, Evangelou E, Dehghan A, O'Regan DP, Wilkins MR, Guo Y, Matthews PM, Rueckert D. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat Med 2020;26:1654-1662. Go to original source... Go to PubMed...
    36. Adao R, Guzik TJ. Inside the heart of COVID-19. Cardiovasc Res 2020;116:e59-e61. Go to original source... Go to PubMed...
    37. Libby P, Luscher T. COVID-19 is, in the end, an endothelial disease. Eur Heart J 2020;41:3038-3044. Go to original source... Go to PubMed...
    38. Evans PC, Rainger G, Mason JC, Guzik TJ, Osto E, Stamataki Z, Neil D, Hoefer IE, Fragiadaki M, Waltenberger J, Weber C, Bochaton-Piallat ML, Back M. Endothelial dysfunction in COVID-19: a position paper of the ESC Working Group for Atherosclerosis and Vascular Biology, and the ESC Council of Basic Cardiovascular Science. Cardiovasc Res 2020. Go to original source... Go to PubMed...
    39. Bachtiger P, Peters NS, Walsh SL. Machine learning for COVID-19-asking the right questions. Lancet Digit Health 2020;2:e391-e392. Go to original source... Go to PubMed...
    40. Linschoten M, Asselbergs FW. CAPACITY-COVID: a European Registry to determine the role of cardiovascular disease in the COVID-19 pandemic. Eur Heart J 2020;41:1795-1796. Go to original source... Go to PubMed...
    41. Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, Xie Z, Ma Y, Robson PM, Chung M, Bernheim A, Mani V, Calcagno C, Li K, Li S, Shan H, Lv J, Zhao T, Xia J, Long Q, Steinberger S, Jacobi A, Deyer T, Luksza M, Liu F, Little BP, Fayad ZA, Yang Y. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med 2020;26:1224-1228. Go to original source... Go to PubMed...
    42. McRae MP, Simmons GW, Christodoulides NJ, Lu Z, Kang SK, Fenyo D, Alcorn T, Dapkins IP, Sharif I, Vurmaz D, Modak SS, Srinivasan K, Warhadpande S, Shrivastav R, McDevitt JT. Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19. Lab Chip 2020;20:2075-2085. Go to original source... Go to PubMed...
    43. Olin JW, Di Narzo AF, d'Escamard V, Kadian-Dodov D, Cheng H, Georges A, King A, Thomas A, Barwari T, Michelis KC,Bouchareb R, Bander E, Anyanwu A, Stelzer P, Filsoufi F, Florman S, Civelek M, Debette S, Jeunemaitre X, Björkegren JLM, Mayr M, Bouatia-Naji N, Hao K, Kovacic JC. A plasma proteogenomic signature for fibromuscular dysplasia. Cardiovasc Res 2020;116:63-77. Go to original source... Go to PubMed...
    44. Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, Wang X, Lv B, Xu B, Song X, Zhang Yj Cheng X, Huang W, Pu J, Zhang Q, Xia Y, Du B, Ji X, Zheng Z. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J 2020;41:4400-4411. Go to original source... Go to PubMed...
    45. Kotanidis CP, Antoniades C. Selfies in cardiovascular medicine: welcome to a new era of medical diagnostics. Eur Heart J 2020;41:4412-4414. Go to original source... Go to PubMed...
    46. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579. Go to original source... Go to PubMed...
    47. Minssen T, Gerke S, Aboy M, Price N, Cohen G. Regulatory responses to medical machine learning. J Law Biosci 2020;46:1-18. Go to original source... Go to PubMed...

    Return to the content


    Cor et Vasa

    You are accessing a site intended for medical professionals, not the lay public. The site may also contain information that is intended only for persons authorized to prescribe and dispense medicinal products for human use.

    I therefore confirm that I am a healthcare professional under Act 40/1995 Coll. as amended by later regulations and that I have read the definition of a healthcare professional.


    No
    Yes