Electrocardiography Meets ArtificialIntelligence: Shaping the Future of HeartFailure Prediction : A Comprehensive Review

Electrocardiography Meets ArtificialIntelligence: Shaping the Future of HeartFailure Prediction : A Comprehensive Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Electrocardiography Meets ArtificialIntelligence: Shaping the Future of HeartFailure Prediction : A Comprehensive Review Faiza Hadid Blaiech, JAMEL HATTAY, Mohsen Machhout This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8608402/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Heart failure (HF) remains a leading cause of morbidity and mortality worldwide,and early detection and risk stratification are critical for improving patientoutcomes. Recent advances in artificial intelligence (AI) and deep learning (DL)have transformed electrocardiogram (ECG) analysis, enabling more accurate predictionof HF and its complications. This review synthesizes current literatureon AI- and DL-based ECG models for HF detection, prognosis, and treatment response,highlighting progress from traditional feature-based methods to end-toenddeep learning architectures, multimodal integration, and explainable AI approaches.Despite these advances, significant gaps remain, including underrepresentationof HFpEF and pre-clinical HF, limited modeling of comorbidities, scarceprospective validation, and insufficient attention to pediatric and special populations.We discuss these unmet needs and future research directions, emphasizingthe potential of AI-enabled ECG analysis to transform HF diagnosis, monitoring,and personalized care. heart failure electrocardiography artificial intelligence deep learning prediction ejection fraction review Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8608402","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574959585,"identity":"a3adf8eb-ff84-469b-b8aa-466fc7c893d6","order_by":0,"name":"Faiza Hadid Blaiech","email":"","orcid":"","institution":"University of Monastir","correspondingAuthor":false,"prefix":"","firstName":"Faiza","middleName":"Hadid","lastName":"Blaiech","suffix":""},{"id":574959586,"identity":"fef1ded4-7185-4023-9c51-3d5540d64c89","order_by":1,"name":"JAMEL HATTAY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABOUlEQVRIie2PP0vDQBTAX4icSzHrg4D5CilCqdjSr3KHoEvj0iWCYCWQDJ5kzeZXaClUx4SDdjDdHRxShG6FuEgm8dpaEJI4O9yPu+P9uR+8B6BQ/EO0oXwyiHdZLi/Z1dp/K1QqDSKTiP4oMeCuH9d4e0VvUNj/rFf04IVlFN6snuWtzG4hro4Mcfeeu3hsBPdU/3guD8adiU1h1eSEtMw+FQOCzLPjFE8wXYwgSSt2caZIQWicwFZhPmo+Jj6yETpS8ctKuH4qpNLj5PDTbG8UIwmK5Atva5XImcr1BeOk0TJho4A8yRCpXausJ7IrzvmsPzjlF5dyMOZhOsNmJHeJF2WlGTrjPHdFN/Dm49eic8Yew/kyd286lhE8jLPrCmW4ee1tfICldlyqAFi/Yj2v+KBQKBQK+AZiP3aKEOx+hQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Monastir","correspondingAuthor":true,"prefix":"","firstName":"JAMEL","middleName":"","lastName":"HATTAY","suffix":""},{"id":574959587,"identity":"9a245581-db7c-405f-a7ed-965fe5f545a1","order_by":2,"name":"Mohsen Machhout","email":"","orcid":"","institution":"University of Monastir","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Machhout","suffix":""}],"badges":[],"createdAt":"2026-01-15 08:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8608402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8608402/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100432150,"identity":"30cbbe73-00f2-4366-8a74-e22308f394a9","added_by":"auto","created_at":"2026-01-16 15:02:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":240936,"visible":true,"origin":"","legend":"","description":"","filename":"Heartfailurepaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8608402/v1/0831c3c7413cac32c69275b9.pdf"},{"id":100432122,"identity":"7dcad8bb-4008-4c10-8ae5-82b743261170","added_by":"auto","created_at":"2026-01-16 15:02:35","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4331,"visible":true,"origin":"","legend":"","description":"","filename":"bc4401a7c06740508e518cf96aa765f6.json","url":"https://assets-eu.researchsquare.com/files/rs-8608402/v1/7166c2d1a041415c491f96dc.json"},{"id":100432151,"identity":"3532f9ba-2b3d-4c34-922f-9f9b77fab208","added_by":"auto","created_at":"2026-01-16 15:02:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":300361,"visible":true,"origin":"","legend":"","description":"","filename":"Heartfailurepaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8608402/v1_covered_cf15a31b-6933-4868-b287-dfd0e401781f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Electrocardiography Meets ArtificialIntelligence: Shaping the Future of HeartFailure Prediction : A Comprehensive Review","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heart failure, electrocardiography, artificial intelligence, deep learning, prediction, ejection fraction, review","lastPublishedDoi":"10.21203/rs.3.rs-8608402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8608402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Heart failure (HF) remains a leading cause of morbidity and mortality worldwide,and early detection and risk stratification are critical for improving patientoutcomes. Recent advances in artificial intelligence (AI) and deep learning (DL)have transformed electrocardiogram (ECG) analysis, enabling more accurate predictionof HF and its complications. This review synthesizes current literatureon AI- and DL-based ECG models for HF detection, prognosis, and treatment response,highlighting progress from traditional feature-based methods to end-toenddeep learning architectures, multimodal integration, and explainable AI approaches.Despite these advances, significant gaps remain, including underrepresentationof HFpEF and pre-clinical HF, limited modeling of comorbidities, scarceprospective validation, and insufficient attention to pediatric and special populations.We discuss these unmet needs and future research directions, emphasizingthe potential of AI-enabled ECG analysis to transform HF diagnosis, monitoring,and personalized care.","manuscriptTitle":"Electrocardiography Meets ArtificialIntelligence: Shaping the Future of HeartFailure Prediction : A Comprehensive Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 15:02:26","doi":"10.21203/rs.3.rs-8608402/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ce81d99-b9d4-447a-9f32-1dcd843db837","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T09:30:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 15:02:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8608402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8608402","identity":"rs-8608402","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}