{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T01:44:05Z","timestamp":1790214245572,"version":"4.1.0"},"reference-count":128,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.<\/jats:p>","DOI":"10.1093\/eurjpc\/zwae008","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T16:34:32Z","timestamp":1704731672000},"page":"470-482","source":"Crossref","is-referenced-by-count":48,"title":["Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete\u2019s heart?"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1558-4857","authenticated-orcid":false,"given":"Stefano","family":"Palermi","sequence":"first","affiliation":[{"name":"Public Health Department, University of Naples Federico II , via Pansini 5, 80131 Naples ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Vecchiato","sequence":"additional","affiliation":[{"name":"Sports and Exercise Medicine Division, Department of Medicine, University of Padova , 35128 Padova ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9475-1507","authenticated-orcid":false,"given":"Andrea","family":"Saglietto","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Cardiovascular and Thoracic Department, \u2018Citta della Salute e della Scienza\u2019 Hospital , 10129 Turin ,","place":["Italy"]},{"name":"Department of Medical Sciences, University of Turin , 10129 Turin ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3089-1222","authenticated-orcid":false,"given":"David","family":"Niederseer","sequence":"additional","affiliation":[{"name":"Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, University of Zurich , 8091 Zurich ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1334-3286","authenticated-orcid":false,"given":"David","family":"Oxborough","sequence":"additional","affiliation":[{"name":"Research Institute for Sport and Exercise Sciences, Liverpool John Moores University , Liverpool ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Ortega-Martorell","sequence":"additional","affiliation":[{"name":"Data Science Research Centre, Liverpool John Moores University , Liverpool ,","place":["UK"]},{"name":"Liverpool Centre for Cardiovascular Science, Liverpool John Moores University , Liverpool ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5679-7501","authenticated-orcid":false,"given":"Ivan","family":"Olier","sequence":"additional","affiliation":[{"name":"Data Science Research Centre, Liverpool John Moores University , Liverpool ,","place":["UK"]},{"name":"Liverpool Centre for Cardiovascular Science, Liverpool John Moores University , Liverpool ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9693-4083","authenticated-orcid":false,"given":"Silvia","family":"Castelletti","sequence":"additional","affiliation":[{"name":"Cardiology Department, Istituto Auxologico Italiano IRCCS , 20149 Milan ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Baggish","sequence":"additional","affiliation":[{"name":"Cardiovascular Performance Program, Massachusetts General Hospital , Boston, MA 02114 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Maffessanti","sequence":"additional","affiliation":[{"name":"Maria Cecilia Hospital, GVM Care&Research , Cotignola ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Biffi","sequence":"additional","affiliation":[{"name":"Med-Ex, Medicine & Exercise, Medical Partner Scuderia Ferrari , 00187 Rome ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9698-9145","authenticated-orcid":false,"given":"Antonello","family":"D\u2019Andrea","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Umberto I Hospital , 84014 Nocera Inferiore ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Zorzi","sequence":"additional","affiliation":[{"name":"Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova , 35128 Padova ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Cavarretta","sequence":"additional","affiliation":[{"name":"Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome , 04100 Latina ,","place":["Italy"]},{"name":"Mediterranea Cardiocentro , 80122 Naples ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Flavio","family":"D\u2019Ascenzi","sequence":"additional","affiliation":[{"name":"Department of Medical Biotechnologies, Division of Cardiology, University of Siena , 53100 Siena ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"2025041611021522700_zwae008-B1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.tcm.2021.06.001","article-title":"Causes of sudden cardiac death in young athletes and non-athletes: systematic review and meta-analysis: sudden cardiac death in the young","volume":"32","author":"D\u2019Ascenzi","year":"2022","journal-title":"Trends Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B2","doi-asserted-by":"crossref","first-page":"271","DOI":"10.4330\/wjc.v13.i8.271","article-title":"Potential role of an athlete-focused echocardiogram in sports eligibility","volume":"13","author":"Palermi","year":"2021","journal-title":"World J Cardiol"},{"key":"2025041611021522700_zwae008-B3","doi-asserted-by":"crossref","first-page":"5126","DOI":"10.3390\/jcm10215126","article-title":"The role of multimodality imaging in athlete\u2019s heart diagnosis: current status and future directions","volume":"10","author":"D\u2019Andrea","year":"2021","journal-title":"J Clin Med"},{"key":"2025041611021522700_zwae008-B4","doi-asserted-by":"crossref","first-page":"151","DOI":"10.31083\/j.rcm2405151","article-title":"Athlete\u2019s heart: a cardiovascular step-by-step multimodality approach","volume":"24","author":"Palermi","year":"2023","journal-title":"Rev Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B5","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1007\/s00246-012-0244-5","article-title":"Primary prevention of sudden cardiac death of the young athlete: the controversy about the screening electrocardiogram and its innovative artificial intelligence solution","volume":"33","author":"Chang","year":"2012","journal-title":"Pediatr Cardiol"},{"key":"2025041611021522700_zwae008-B6","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.tcm.2020.11.007","article-title":"Artificial intelligence in cardiology","volume":"32","author":"Itchhaporia","year":"2022","journal-title":"Trends Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B7","first-page":"14","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"2025041611021522700_zwae008-B8","first-page":"1","volume-title":"Ethics and governance of artificial intelligence for health: WHO guidance","author":"World Health Organization","year":"2021"},{"key":"2025041611021522700_zwae008-B9","doi-asserted-by":"publisher","DOI":"10.1787\/eedfee77-en","volume-title":"Artificial Intelligence in Society","author":"OECD","year":"2019"},{"key":"2025041611021522700_zwae008-B10","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/s42254-023-00584-1","article-title":"How to edit anthropomorphic language about artificial intelligence","volume":"5","year":"2023","journal-title":"Nat Rev Phys"},{"key":"2025041611021522700_zwae008-B11","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.jcin.2019.04.048","article-title":"Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance","volume":"12","author":"Sardar","year":"2019","journal-title":"JACC Cardiovasc Interv"},{"key":"2025041611021522700_zwae008-B12","first-page":"154","article-title":"Artificial intelligence advances in the world of cardiovascular imaging","volume":"10","author":"Patel","year":"2022","journal-title":"Healthcare (Switzerland)"},{"key":"2025041611021522700_zwae008-B13","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1042\/ETLS20210246","article-title":"Artificial intelligence, machine learning, and deep learning for clinical outcome prediction","volume":"5","author":"Pettit","year":"2021","journal-title":"Emerg Top Life Sci"},{"key":"2025041611021522700_zwae008-B14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fcvm.2019.00133","article-title":"Artificial intelligence will transform cardiac imaging\u2014opportunities and challenges","volume":"6","author":"Petersen","year":"2019","journal-title":"Front Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B15","doi-asserted-by":"crossref","first-page":"e0174944","DOI":"10.1371\/journal.pone.0174944","article-title":"Can machine-learning improve cardiovascular risk prediction using routine clinical data?","volume":"12","author":"Weng","year":"2017","journal-title":"PLoS One"},{"key":"2025041611021522700_zwae008-B16","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1016\/j.jacc.2017.03.571","article-title":"Artificial intelligence in precision cardiovascular medicine","volume":"69","author":"Krittanawong","year":"2017","journal-title":"J Am Coll Cardiol"},{"key":"2025041611021522700_zwae008-B17","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11936-019-0728-1","article-title":"Artificial intelligence in cardiovascular medicine","volume":"21","author":"Seetharam","year":"2019","journal-title":"Curr Treat Options Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B18","doi-asserted-by":"crossref","first-page":"618849","DOI":"10.3389\/fcvm.2020.618849","article-title":"The role of artificial intelligence in cardiovascular imaging: state of the art review","volume":"7","author":"Seetharam","year":"2020","journal-title":"Front Cardiovasc Med"},{"issue":"Suppl","key":"2025041611021522700_zwae008-B19","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/0022-0736(90)90102-8","article-title":"Self-learning neural networks in electrocardiography","volume":"23","author":"Dassen","year":"1990","journal-title":"J Electrocardiol"},{"key":"2025041611021522700_zwae008-B20","first-page":"382","article-title":"The athlete\u2019s heart and machine learning: a review of current implementations and gaps for future research","volume":"9","author":"Bellfield","year":"2022","journal-title":"J Cardiovasc Dev Dis"},{"key":"2025041611021522700_zwae008-B21","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3390\/sports11020034","article-title":"The eSports medicine: pre-participation screening and injuries management\u2014an update","volume":"11","author":"Rossoni","year":"2023","journal-title":"Sports (Basel)"},{"key":"2025041611021522700_zwae008-B22","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1161\/01.CIR.94.4.850","article-title":"Cardiovascular preparticipation screening of competitive athletes. A statement for health professionals from the Sudden Death Committee (clinical cardiology) and Congenital Cardiac Defects Committee (cardiovascular disease in the young), American Heart Association","volume":"94","author":"Maron","year":"1996","journal-title":"Circulation"},{"key":"2025041611021522700_zwae008-B23","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1515\/jbcpp-2022-0109","article-title":"Limited diagnostic value of questionnaire-based pre-participation screening algorithms: a \u201crisk-exposed\u201d approach to sports activity","volume":"33","author":"Palermi","year":"2022","journal-title":"J Basic Clin Physiol Pharmacol"},{"key":"2025041611021522700_zwae008-B24","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/978-3-319-02753-1_23","article-title":"What we found on our way to building a classifier: a critical analysis of the AHA screening questionnaire","volume":"8211 LNAI","author":"Rahman","year":"2013","journal-title":"Lecture Notes Comput Sci"},{"key":"2025041611021522700_zwae008-B25","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ijmedinf.2006.01.006","article-title":"Patient-directed intelligent and interactive computer medical history-gathering systems: a utility and feasibility study in the emergency department","volume":"76","author":"Benaroia","year":"2007","journal-title":"Int J Med Inform"},{"key":"2025041611021522700_zwae008-B26","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"2025041611021522700_zwae008-B27","doi-asserted-by":"crossref","first-page":"e21056","DOI":"10.2196\/21056","article-title":"Impact of a commercial artificial intelligence\u2013driven patient self-assessment solution on waiting times at general internal medicine outpatient departments: retrospective study","volume":"8","author":"Harada","year":"2020","journal-title":"JMIR Med Inform"},{"key":"2025041611021522700_zwae008-B28","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med"},{"key":"2025041611021522700_zwae008-B29","first-page":"1008","article-title":"The athletic preparticipation evaluation: cardiovascular assessment","volume":"75","author":"Giese","year":"2007","journal-title":"Am Fam Physician"},{"key":"2025041611021522700_zwae008-B30","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1136\/bjsports-2019-101718","article-title":"Do \u201cpathologic\u201d cardiac murmurs in adolescents identify structural heart disease? An evaluation of 15","volume":"56","author":"Austin","year":"2022","journal-title":"Br J Sports Med"},{"key":"2025041611021522700_zwae008-B31","first-page":"460","article-title":"AI used to detect cardiac murmurs","volume":"18","author":"Lim","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"2025041611021522700_zwae008-B32","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s00246-018-2036-z","article-title":"Artificial intelligence-assisted auscultation of heart murmurs: validation by virtual clinical trial","volume":"40","author":"Thompson","year":"2019","journal-title":"Pediatr Cardiol"},{"key":"2025041611021522700_zwae008-B33","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1177\/1941738117695221","article-title":"The diagnostic utility of computer-assisted auscultation for the early detection of cardiac murmurs of structural origin in the periodic health evaluation","volume":"9","author":"Viviers","year":"2017","journal-title":"Sports Health"},{"key":"2025041611021522700_zwae008-B34","doi-asserted-by":"crossref","first-page":"2616","DOI":"10.1161\/CIRCULATIONAHA.107.733519","article-title":"Electrocardiograms should be included in preparticipation screening of athletes","volume":"116","author":"Myerburg","year":"2007","journal-title":"Circulation"},{"key":"2025041611021522700_zwae008-B35","doi-asserted-by":"crossref","first-page":"269","DOI":"10.7326\/0003-4819-152-5-201003020-00004","article-title":"Cardiovascular screening in college athletes with and without electrocardiography: a cross-sectional study","volume":"152","author":"Baggish","year":"2010","journal-title":"Ann Intern Med"},{"key":"2025041611021522700_zwae008-B36","article-title":"Cardiologist-level arrhythmia detection with convolutional neural networks","author":"Rajpurkar","year":"2017","journal-title":"ArXiv"},{"key":"2025041611021522700_zwae008-B37","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"Siontis","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"2025041611021522700_zwae008-B38","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.echo.2020.02.009","article-title":"Recommendations on the use of multimodality cardiovascular imaging in young adult competitive athletes: a report from the American Society of Echocardiography in collaboration with the Society of Cardiovascular Computed Tomography and the Society for Car","volume":"33","author":"Baggish","year":"2020","journal-title":"J Am Soc Echocardiogr"},{"key":"2025041611021522700_zwae008-B39","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.cvdhj.2020.11.006","article-title":"Prediction of premature ventricular complex origins using artificial intelligence-enabled algorithms","volume":"2","author":"Nakamura","year":"2021","journal-title":"Cardiovasc Digit Health J"},{"key":"2025041611021522700_zwae008-B40","doi-asserted-by":"crossref","first-page":"20495","DOI":"10.1038\/s41598-020-77599-6","article-title":"Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography","volume":"10","author":"Cho","year":"2020","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B41","doi-asserted-by":"crossref","first-page":"4919","DOI":"10.3390\/s22134919","article-title":"Interpretable assessment of ST-segment deviation in ECG time series","volume":"22","author":"Jurado","year":"2022","journal-title":"Sensors (Basel)"},{"key":"2025041611021522700_zwae008-B42","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.jacc.2019.12.030","article-title":"Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram","volume":"75","author":"Ko","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"2025041611021522700_zwae008-B43","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.amjcard.2021.06.021","article-title":"Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy","volume":"155","author":"Shrivastava","year":"2021","journal-title":"Am J Cardiol"},{"key":"2025041611021522700_zwae008-B44","doi-asserted-by":"crossref","first-page":"e3644","DOI":"10.1002\/cnm.3644","article-title":"Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC","volume":"38","author":"Papageorgiou","year":"2022","journal-title":"Int J Numer Method Biomed Eng"},{"key":"2025041611021522700_zwae008-B45","doi-asserted-by":"crossref","first-page":"euac053.558","DOI":"10.1093\/europace\/euac053.558","article-title":"Artificial intelligence algorithms for the recognition of Brugada type 1 pattern on standard 12-leads ECG","volume":"24","author":"Vozzi","year":"2022","journal-title":"EP Europace"},{"key":"2025041611021522700_zwae008-B46","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1001\/jamacardio.2020.7422","article-title":"Use of artificial intelligence and deep neural networks in evaluation of patients with electrocardiographically concealed long QT syndrome from the surface 12-lead electrocardiogram","volume":"6","author":"Bos","year":"2021","journal-title":"JAMA Cardiol"},{"key":"2025041611021522700_zwae008-B47","doi-asserted-by":"crossref","first-page":"8045","DOI":"10.1038\/s41598-021-87631-y","article-title":"Accessory pathway analysis using a multimodal deep learning model","volume":"11","author":"Nishimori","year":"2021","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B48","doi-asserted-by":"crossref","first-page":"1411220","DOI":"10.1080\/23311916.2017.1411220","article-title":"Automated detection of heart defects in athletes based on electrocardiography and artificial neural network","volume":"4","author":"Adetiba","year":"2017","journal-title":"Cogent Eng"},{"key":"2025041611021522700_zwae008-B49","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1007\/978-3-030-17935-9_47","article-title":"Development of an ECG smart jersey based on next generation computing for automated detection of heart defects among athletes","volume":"11466 LNBI","author":"Adetiba","year":"2019","journal-title":"Lecture Notes Comput Sci"},{"key":"2025041611021522700_zwae008-B50","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jelectrocard.2018.02.004","article-title":"Magnetocardiographic classification and non-invasive electro-anatomical imaging of outflow tract ventricular arrhythmias in recreational sport activity practitioners","volume":"51","author":"Lombardi","year":"2018","journal-title":"J Electrocardiol"},{"key":"2025041611021522700_zwae008-B51","first-page":"1065","article-title":"ECG signal analysis for troponin level assessment and coronary artery disease detection: the NEEDED study 2014","author":"D\u0142ugosz","year":"2018"},{"key":"2025041611021522700_zwae008-B52","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.jelectrocard.2023.12.009","article-title":"Validation of an automated artificial intelligence system for 12\u2013lead ECG interpretation","volume":"82","author":"Herman","year":"2024","journal-title":"J Electrocardiol"},{"key":"2025041611021522700_zwae008-B53","doi-asserted-by":"crossref","first-page":"102141","DOI":"10.1016\/j.eclinm.2023.102141","article-title":"Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study","volume":"63","author":"Miura","year":"2023","journal-title":"EClinicalMedicine"},{"key":"2025041611021522700_zwae008-B54","doi-asserted-by":"crossref","first-page":"e026196","DOI":"10.1161\/JAHA.122.026196","article-title":"Evaluation of an ambulatory ECG analysis platform using deep neural networks in routine clinical practice","volume":"11","author":"Fiorina","year":"2022","journal-title":"J Am Heart Assoc"},{"key":"2025041611021522700_zwae008-B55","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.3390\/diagnostics12051256","article-title":"The complexity of the arterial blood pressure regulation during the stress test","volume":"12","author":"Qammar","year":"2022","journal-title":"Diagnostics (Basel)"},{"key":"2025041611021522700_zwae008-B56","doi-asserted-by":"crossref","first-page":"826","DOI":"10.3390\/s23020826","article-title":"Machine learning models for the automatic detection of exercise thresholds in cardiopulmonary exercising tests: from regression to generation to explanation","volume":"23","author":"Zignoli","year":"2023","journal-title":"Sensors (Basel)"},{"key":"2025041611021522700_zwae008-B57","doi-asserted-by":"crossref","first-page":"R115","DOI":"10.1530\/ERP-18-0056","article-title":"Artificial intelligence and echocardiography","volume":"5","author":"Alsharqi","year":"2018","journal-title":"Echo Res Pract"},{"key":"2025041611021522700_zwae008-B58","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3390\/jimaging9020050","article-title":"The role of artificial intelligence in echocardiography","volume":"9","author":"Barry","year":"2023","journal-title":"J Imaging"},{"key":"2025041611021522700_zwae008-B59","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully automated echocardiogram interpretation in clinical practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"2025041611021522700_zwae008-B60","first-page":"297","article-title":"Echocardiography in the preparticipation screening: an old topic revisited","volume":"24","author":"Donati","year":"2023","journal-title":"J Cardiovasc Med (Hagerstown)"},{"key":"2025041611021522700_zwae008-B61","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41598-022-27211-w","article-title":"A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection","volume":"13","author":"Liu","year":"2023","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B62","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.jcmg.2019.10.013","article-title":"Enhanced diagnosis of severe aortic stenosis using artificial intelligence: a proof-of-concept study of 530,871 echocardiograms","volume":"13","author":"Playford","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025041611021522700_zwae008-B63","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.compbiomed.2016.03.026","article-title":"Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos","volume":"73","author":"Moghaddasi","year":"2016","journal-title":"Comput Biol Med"},{"key":"2025041611021522700_zwae008-B64","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1111\/echo.15475","article-title":"Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: feasibility, accuracy, and reproducibility","volume":"39","author":"Karu\u017eas","year":"2022","journal-title":"Echocardiography"},{"key":"2025041611021522700_zwae008-B65","doi-asserted-by":"crossref","first-page":"1195235","DOI":"10.3389\/fcvm.2023.1195235","article-title":"Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography","volume":"10","author":"Wu","year":"2023","journal-title":"Front Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B66","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s10554-022-02566-3","article-title":"Application of machine learning in screening for congenital heart diseases using fetal echocardiography","volume":"38","author":"Truong","year":"2022","journal-title":"Int J Cardiovasc Imaging"},{"key":"2025041611021522700_zwae008-B67","doi-asserted-by":"crossref","first-page":"e001495","DOI":"10.1136\/openhrt-2020-001495","article-title":"Echocardiographic screening for the anomalous aortic origin of coronary arteries","volume":"8","author":"Bianco","year":"2021","journal-title":"Open Heart"},{"key":"2025041611021522700_zwae008-B68","doi-asserted-by":"crossref","first-page":"470","DOI":"10.4330\/wjc.v9.i6.470","article-title":"Cardiac damage in athlete\u2019s heart: when the \u201csupernormal\u201d heart fails!","volume":"9","author":"Carbone","year":"2017","journal-title":"World J Cardiol"},{"key":"2025041611021522700_zwae008-B69","doi-asserted-by":"publisher","author":"Niederseer","DOI":"10.1136\/heartjnl-2020-317996"},{"key":"2025041611021522700_zwae008-B70","first-page":"457","article-title":"Grey zones in cardiovascular adaptations to physical exercise: how to navigate in the echocardiographic evaluation of the athlete\u2019s heart","volume":"21","author":"D\u2019Andrea","year":"2020","journal-title":"G Ital Cardiol (Rome)"},{"key":"2025041611021522700_zwae008-B71","doi-asserted-by":"crossref","first-page":"20998","DOI":"10.1038\/s41598-022-25467-w","article-title":"Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model","volume":"12","author":"Hwang","year":"2022","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B72","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1016\/j.jfma.2021.10.017","article-title":"Data-driven clustering supports adaptive remodeling of athlete\u2019s hearts: an echocardiographic study from the Taipei Summer Universiade","volume":"121","author":"Huang","year":"2022","journal-title":"J Formos Med Assoc"},{"key":"2025041611021522700_zwae008-B73","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","article-title":"Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography","volume":"68","author":"Narula","year":"2016","journal-title":"J Am Coll Cardiol"},{"key":"2025041611021522700_zwae008-B74","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.jcmg.2021.10.013","article-title":"Automated echocardiographic detection of severe coronary artery disease using artificial intelligence","volume":"15","author":"Upton","year":"2022","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025041611021522700_zwae008-B75","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.jcmg.2017.11.016","article-title":"The role of cardiovascular magnetic resonance imaging in the assessment of highly trained athletes","volume":"11","author":"Gati","year":"2018","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025041611021522700_zwae008-B76","doi-asserted-by":"crossref","first-page":"945726","DOI":"10.3389\/fcvm.2022.945726","article-title":"Artificial intelligence in cardiology: hope for the future and power for the present","volume":"9","author":"Karatzia","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"2025041611021522700_zwae008-B77","doi-asserted-by":"crossref","first-page":"e0212272","DOI":"10.1371\/journal.pone.0212272","article-title":"Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data","volume":"14","author":"Biasiolli","year":"2019","journal-title":"PLoS One"},{"key":"2025041611021522700_zwae008-B78","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.3390\/jcm11102866","article-title":"The applications of artificial intelligence in cardiovascular magnetic resonance-a comprehensive review","volume":"11","author":"Argentiero","year":"2022","journal-title":"J Clin Med"},{"key":"2025041611021522700_zwae008-B79","doi-asserted-by":"crossref","first-page":"192","DOI":"10.2459\/JCM.0000000000000924","article-title":"Bridging the gap between hypertrabeculation phenotype, noncompaction phenotype and left ventricular noncompaction cardiomyopathy","volume":"21","author":"Vergani","year":"2020","journal-title":"J Cardiovasc Med (Hagerstown)"},{"issue":"Suppl 2","key":"2025041611021522700_zwae008-B80","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1475-925X-14-S2-S7","article-title":"cMRI-BED: a novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification","volume":"14","author":"Gopalakrishnan","year":"2015","journal-title":"Biomed Eng Online"},{"key":"2025041611021522700_zwae008-B81","doi-asserted-by":"crossref","first-page":"101792","DOI":"10.1016\/j.media.2020.101792","article-title":"Handling confounding variables in statistical shape analysis\u2014application to cardiac remodelling","volume":"65","author":"Bernardino","year":"2020","journal-title":"Med Image Anal"},{"key":"2025041611021522700_zwae008-B82","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"2025041611021522700_zwae008-B83","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1148\/radiol.2019190101","article-title":"Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure","volume":"292","author":"Baessler","year":"2019","journal-title":"Radiology"},{"key":"2025041611021522700_zwae008-B84","doi-asserted-by":"crossref","first-page":"23596","DOI":"10.1038\/s41598-021-02971-z","article-title":"Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes","volume":"11","author":"Antonopoulos","year":"2021","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B85","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/eurheartj\/ehaa605","article-title":"2020 ESC guidelines on sports cardiology and exercise in patients with cardiovascular disease","volume":"42","author":"Pelliccia","year":"2021","journal-title":"Eur Heart J"},{"key":"2025041611021522700_zwae008-B86","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.media.2016.04.004","article-title":"Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks","volume":"34","author":"Wolterink","year":"2016","journal-title":"Med Image Anal"},{"key":"2025041611021522700_zwae008-B87","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1093\/eurjpc\/zwac080","article-title":"Indications and utility of cardiac genetic testing in athletes","volume":"29","author":"Castelletti","year":"2022","journal-title":"Eur J Prev Cardiol"},{"key":"2025041611021522700_zwae008-B88","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ijcard.2022.05.071","article-title":"Molecular genetic testing in athletes: why and when a position statement from the Italian society of sports cardiology","volume":"364","author":"Castelletti","year":"2022","journal-title":"Int J Cardiol"},{"key":"2025041611021522700_zwae008-B89","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1177\/2047487320934265","article-title":"Yield and clinical significance of genetic screening in elite and amateur athletes","volume":"28","author":"Limongelli","year":"2021","journal-title":"Eur J Prev Cardiol"},{"key":"2025041611021522700_zwae008-B90","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1161\/CIRCULATIONAHA.118.034208","article-title":"Diagnostic yield of genetic testing in young athletes with T-wave inversion","volume":"138","author":"Sheikh","year":"2018","journal-title":"Circulation"},{"key":"2025041611021522700_zwae008-B91","first-page":"279","article-title":"Artificial intelligence and cardiovascular genetics","volume":"12","author":"Krittanawong","year":"2022","journal-title":"Life (Basel)"},{"key":"2025041611021522700_zwae008-B92","doi-asserted-by":"crossref","first-page":"793","DOI":"10.7150\/ijms.33967","article-title":"A prediction model of essential hypertension based on genetic and environmental risk factors in Northern Han Chinese","volume":"16","author":"Li","year":"2019","journal-title":"Int J Med Sci"},{"key":"2025041611021522700_zwae008-B93","doi-asserted-by":"crossref","first-page":"9355","DOI":"10.1038\/s41598-018-27695-5","article-title":"Detection of genetic cardiac diseases by Ca(2+) transient profiles using machine learning methods","volume":"8","author":"Juhola","year":"2018","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B94","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1186\/s12918-017-0474-5","article-title":"Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq\u00ae and Framingham Heart Study cohorts","volume":"11","author":"Oguz","year":"2017","journal-title":"BMC Syst Biol"},{"key":"2025041611021522700_zwae008-B95","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.yjmcc.2018.04.006","article-title":"Neural\/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype","volume":"119","author":"Burghardt","year":"2018","journal-title":"J Mol Cell Cardiol"},{"key":"2025041611021522700_zwae008-B96","first-page":"CD007130","article-title":"Home-based versus centre-based cardiac rehabilitation","volume":"6","author":"Anderson","year":"2017","journal-title":"Cochrane Database Syst Rev"},{"key":"2025041611021522700_zwae008-B97","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1093\/eurjpc\/zwab007","article-title":"Exercise intensity assessment and prescription in cardiovascular rehabilitation and beyond: why and how: a position statement from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology","volume":"29","author":"Hansen","year":"2022","journal-title":"Eur J Prev Cardiol"},{"key":"2025041611021522700_zwae008-B98","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3390\/sports10030030","article-title":"The effectiveness of adapted personalized motor activity (AMPA) to improve health in individuals with mental disorders and physical comorbidities: a randomized controlled trial","volume":"10","author":"Lamberti","year":"2022","journal-title":"Sports"},{"key":"2025041611021522700_zwae008-B99","first-page":"101080","article-title":"Physical and psychological reconditioning in long COVID syndrome: results of an out-of-hospital exercise and psychological\u2014based rehabilitation program","volume":"41","author":"Compagno","year":"2022","journal-title":"Int J Cardiol Heart Vasc"},{"key":"2025041611021522700_zwae008-B100","doi-asserted-by":"crossref","first-page":"S93","DOI":"10.3349\/ymj.2022.63.S93","article-title":"Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: a systematic review and meta-analysis","volume":"63","author":"Lee","year":"2022","journal-title":"Yonsei Med J"},{"key":"2025041611021522700_zwae008-B101","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1038\/s41569-020-00445-9","article-title":"Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management","volume":"18","author":"Krittanawong","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"2025041611021522700_zwae008-B102","doi-asserted-by":"crossref","first-page":"106541","DOI":"10.1016\/j.cmpb.2021.106541","article-title":"Application of artificial intelligence in wearable devices: opportunities and challenges","volume":"213","author":"Nahavandi","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"2025041611021522700_zwae008-B103","doi-asserted-by":"crossref","first-page":"e14857","DOI":"10.2196\/14857","article-title":"Use of a smart watch for early detection of paroxysmal atrial fibrillation: validation study","volume":"4","author":"Inui","year":"2020","journal-title":"JMIR Cardio"},{"key":"2025041611021522700_zwae008-B104","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.acvd.2020.05.015","article-title":"Diagnostic accuracy of smart gadgets\/wearable devices in detecting atrial fibrillation: a systematic review and meta-analysis","volume":"114","author":"Prasitlumkum","year":"2021","journal-title":"Arch Cardiovasc Dis"},{"key":"2025041611021522700_zwae008-B105","first-page":"e20362","article-title":"Accuracy of detecting atrial fibrillation: a systematic review and meta-analysis of wrist-worn wearable technology","volume":"13","author":"Belani","year":"2021","journal-title":"Cureus"},{"key":"2025041611021522700_zwae008-B106","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1111\/jce.15892","article-title":"Accuracy of the Apple watch for detection of AF: a multicenter experience","volume":"34","author":"Wasserlauf","year":"2023","journal-title":"J Cardiovasc Electrophysiol"},{"key":"2025041611021522700_zwae008-B107","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1001\/jama.2018.20437","article-title":"Wearable devices for cardiac rhythm diagnosis and management","volume":"321","author":"Ip","year":"2019","journal-title":"JAMA"},{"key":"2025041611021522700_zwae008-B108","doi-asserted-by":"crossref","first-page":"379","DOI":"10.21037\/cdt.2019.06.05","article-title":"Accuracy of commercially available heart rate monitors in athletes: a prospective study","volume":"9","author":"Pasadyn","year":"2019","journal-title":"Cardiovasc Diagn Ther"},{"key":"2025041611021522700_zwae008-B109","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1161\/CIRCULATIONAHA.120.048253","article-title":"Validating QT-interval measurement using the Apple watch ECG to enable remote monitoring during the COVID-19 pandemic","volume":"142","author":"Strik","year":"2020","journal-title":"Circulation"},{"key":"2025041611021522700_zwae008-B110","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ijcard.2018.03.097","article-title":"A wearable remote monitoring system for the identification of subjects with a prolonged QT interval or at risk for drug-induced long QT syndrome","volume":"266","author":"Castelletti","year":"2018","journal-title":"Int J Cardiol"},{"key":"2025041611021522700_zwae008-B111","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.3390\/s19122780","article-title":"Wearable real-time heart attack detection and warning system to reduce road accidents","volume":"19","author":"Chowdhury","year":"2019","journal-title":"Sensors (Basel)"},{"key":"2025041611021522700_zwae008-B112","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jelectrocard.2021.03.008","article-title":"Comparison between a 6\u2013lead smartphone ECG and 12\u2013lead ECG in athletes","volume":"66","author":"Orchard","year":"2021","journal-title":"J Electrocardiol"},{"key":"2025041611021522700_zwae008-B113","doi-asserted-by":"crossref","first-page":"2716","DOI":"10.3390\/app12052716","article-title":"Energy efficient framework for a AIoT cardiac arrhythmia detection system wearable during sport","volume":"12","author":"Castillo-Atoche","year":"2022","journal-title":"Appl Sci"},{"key":"2025041611021522700_zwae008-B114","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.jelectrocard.2015.07.010","article-title":"Smartphone ECG aids real time diagnosis of palpitations in the competitive college athlete","volume":"48","author":"Peritz","year":"2015","journal-title":"J Electrocardiol"},{"key":"2025041611021522700_zwae008-B115","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s41746-019-0130-0","article-title":"Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor","volume":"2","author":"Green","year":"2019","journal-title":"NPJ Digit Med"},{"key":"2025041611021522700_zwae008-B116","doi-asserted-by":"crossref","first-page":"630576","DOI":"10.3389\/fspor.2020.630576","article-title":"Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden","volume":"2","author":"Seshadri","year":"2021","journal-title":"Front Sports Act Living"},{"key":"2025041611021522700_zwae008-B117","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","article-title":"Smart wearable devices in cardiovascular care: where we are and how to move forward","volume":"18","author":"Bayoumy","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"2025041611021522700_zwae008-B118","doi-asserted-by":"crossref","first-page":"16744","DOI":"10.1039\/C5NR02732B","article-title":"High performance carbon nanotube\u2014polymer nanofiber hybrid fabrics","volume":"7","author":"Yildiz","year":"2015","journal-title":"Nanoscale"},{"key":"2025041611021522700_zwae008-B119","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1093\/ehjdh\/ztab056","article-title":"Wearables for cardiac monitoring in athletes: precious metal or fool\u2019s gold?","volume":"2","author":"Fanous","year":"2021","journal-title":"Eur Heart J Digit Health"},{"key":"2025041611021522700_zwae008-B120","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/B978-0-12-818438-7.00012-5","volume-title":"Artificial intelligence in healthcare","author":"Gerke","year":"2020"},{"key":"2025041611021522700_zwae008-B121","article-title":"Understanding artificial intelligence ethics andsafety: a guide for the responsible design and implementation of AI systems in the public sector","author":"Leslie","year":"2019","journal-title":"Alan Turing Inst"},{"key":"2025041611021522700_zwae008-B122","doi-asserted-by":"crossref","first-page":"19525","DOI":"10.1038\/s41598-022-23817-2","article-title":"Enhanced survival prediction using explainable artificial intelligence in heart transplantation","volume":"12","author":"Lisboa","year":"2022","journal-title":"Sci Rep"},{"key":"2025041611021522700_zwae008-B123","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3390\/a16040181","article-title":"How to open a black box classifier for tabular data","volume":"16","author":"Walters","year":"2023","journal-title":"Algorithms"},{"key":"2025041611021522700_zwae008-B124","doi-asserted-by":"crossref","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI-explainable artificial intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci Robot"},{"key":"2025041611021522700_zwae008-B125","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3389\/fmed.2020.00027","article-title":"Artificial intelligence in medicine: today and tomorrow","volume":"7","author":"Briganti","year":"2020","journal-title":"Front Med (Lausanne)"},{"key":"2025041611021522700_zwae008-B126","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/1471-2288-6-9","article-title":"Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies","volume":"6","author":"Whiting","year":"2006","journal-title":"BMC Med Res Methodol"},{"key":"2025041611021522700_zwae008-B127","doi-asserted-by":"crossref","first-page":"7923","DOI":"10.3390\/ijerph17217923","article-title":"Predicting cardiovascular risk in athletes: resampling improves classification performance","volume":"17","author":"Barbieri","year":"2020","journal-title":"Int J Environ Res Public Health"},{"key":"2025041611021522700_zwae008-B128","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijmedinf.2006.11.006","article-title":"Predictive data mining in clinical medicine: current issues and guidelines","volume":"77","author":"Bellazzi","year":"2008","journal-title":"Int J Med Inform"}],"container-title":["European Journal of Preventive Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/eurjpc\/advance-article-pdf\/doi\/10.1093\/eurjpc\/zwae008\/56254569\/zwae008.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/eurjpc\/article-pdf\/31\/4\/470\/56831842\/zwae008.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/eurjpc\/article-pdf\/31\/4\/470\/56831842\/zwae008.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T11:02:51Z","timestamp":1744801371000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/eurjpc\/article\/31\/4\/470\/7515174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":128,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,1,10]]},"published-print":{"date-parts":[[2024,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/eurjpc\/zwae008","relation":{},"ISSN":["2047-4873","2047-4881"],"issn-type":[{"value":"2047-4873","type":"print"},{"value":"2047-4881","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3]]},"published":{"date-parts":[[2024,1,10]]}}}