Automated interpretation of fetal cardiac function evaluation from the echocardiogram

Automated interpretation of fetal cardiac function evaluation from the echocardiogram | 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 Article Automated interpretation of fetal cardiac function evaluation from the echocardiogram Caixin Huang, Lihe Zhang, Baihong Xie, Yuting Jiang, Yunxiao Zhu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7630488/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in npj Digital Medicine → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose: Prenatal assessment of fetal cardiac function is crucial for predicting neonatal outcomes, yet manual measurements from fetal echocardiograms are labor-intensive and prone to human error. To address these challenges, we developed a fully automated artificial intelligence (AI) workflow to estimate fetal cardiac function parameters from echocardiograms. Methods: The AI workflow implement a deep learning architecture with capability of real-time detecting and segmenting the potential cardiac structures from the whole echocardiogram. The AI workflow considers a quality control by the predictive confidence and dynamic trends of detected structures, and then effectively provide multiple measurement parameters from the segmentation. We developed and validated the AI workflow using a internal dataset of 52,942 annotated images from 1,940 echocardiograms with the normal singleton fetal hearts at the First Affiliated Hospital of Sun Yat-sen University. We also validated the segmentation performance of the workflow in two external normal dataset (129 echocardiograms at the Seventh Affiliated Hospital of Sun Yat-sen University and 116 echocardiograms at Zhongshan City People’s Hospital), and one internal abnormal dataset at the First Affiliated Hospital of Sun Yat-sen University. We validated the automatic measurements of workflow against manual measurements in the ultrasound instrument with or without the fetal Heart Quantification software measurements by two expert sonographers. The AI workflow further establishes a Z-score model with dynamic consideration of the gestational age information and fetal biometric parameters to standardize the evaluation system of fetal cardiac function. Results: The segmentation of the workflow was accurate, with a mean Dice similarity coefficient greater than 92% and a mean intersection-over-union greater than 85%, over both the internal and external test dataset. Automated measurements showed strong agreement with manual and Fetal HQ measurements, with intra-class correlation coefficients ranging from 0.817 to 0.995 and mean absolute errors ranging from 0.009 to 5.415. Bland-Altman limits of agreement indicated good agreement between automated and human measurements. The mean individual equivalence coefficients for all parameters were less than 0, indicating lower variability in automated measurements compared to manual and fetal HQ measurements. Conclusion: The AI-based workflow can accurately segment fetal cardiac structures and systematically quantify cardiac function parameters with high precision and reliability, comparable to expert manual measurements. This automated approach provide accurate, simple, efficient and repeatable intelligent tool for cardiac function quantification in clinical practice. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research artificial intelligence deep learning fetal echocardiography prenatal ultrasonography Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.pdf VedioS1.mp4 Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in npj Digital Medicine → Version 1 posted Editorial decision: Revision requested 07 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 27 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 16 Sep, 2025 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. 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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-7630488","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529008469,"identity":"c9037beb-75f0-4142-845b-e9ce574a1ae4","order_by":0,"name":"Caixin Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Caixin","middleName":"","lastName":"Huang","suffix":""},{"id":529008471,"identity":"745d838f-8095-4354-9b7b-3ccb6282057d","order_by":1,"name":"Lihe Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen 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echocardiogram","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, deep learning, fetal echocardiography, prenatal ultrasonography","lastPublishedDoi":"10.21203/rs.3.rs-7630488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7630488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003ePrenatal assessment of fetal cardiac function is crucial for predicting neonatal outcomes, yet manual measurements from fetal echocardiograms are labor-intensive and prone to human error. To address these challenges, we developed a fully automated artificial intelligence (AI) workflow to estimate fetal cardiac function parameters from echocardiograms.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThe AI workflow implement a deep learning architecture with capability of real-time detecting and segmenting the potential cardiac structures from the whole echocardiogram. The AI workflow considers a quality control by the predictive confidence and dynamic trends of detected structures, and then effectively provide multiple measurement parameters from the segmentation. We developed and validated the AI workflow using a internal dataset of 52,942 annotated images from 1,940 echocardiograms with the normal singleton fetal hearts at the First Affiliated Hospital of Sun Yat-sen University. We also validated the segmentation performance of the workflow in two external normal dataset (129 echocardiograms at the Seventh Affiliated Hospital of Sun Yat-sen University and 116 echocardiograms at Zhongshan City People\u0026rsquo;s Hospital), and one internal abnormal dataset at the First Affiliated Hospital of Sun Yat-sen University. We validated the automatic measurements of workflow against manual measurements in the ultrasound instrument with or without the fetal Heart Quantification software measurements by two expert sonographers. The AI workflow further establishes a Z-score model with dynamic consideration of the gestational age information and fetal biometric parameters to standardize the evaluation system of fetal cardiac function.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe segmentation of the workflow was accurate, with a mean Dice similarity coefficient greater than 92% and a mean intersection-over-union greater than 85%, over both the internal and external test dataset. Automated measurements showed strong agreement with manual and Fetal HQ measurements, with intra-class correlation coefficients ranging from 0.817 to 0.995 and mean absolute errors ranging from 0.009 to 5.415. Bland-Altman limits of agreement indicated good agreement between automated and human measurements. The mean individual equivalence coefficients for all parameters were less than 0, indicating lower variability in automated measurements compared to manual and fetal HQ measurements.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThe AI-based workflow can accurately segment fetal cardiac structures and systematically quantify cardiac function parameters with high precision and reliability, comparable to expert manual measurements. This automated approach provide accurate, simple, efficient and repeatable intelligent tool for cardiac function quantification in clinical practice.\u003c/p\u003e","manuscriptTitle":"Automated interpretation of fetal cardiac function evaluation from the echocardiogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 19:25:52","doi":"10.21203/rs.3.rs-7630488/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-07T19:00:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T19:09:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251767370715650206135541651380711713363","date":"2025-11-04T06:33:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19952706674170692864686835042750195029","date":"2025-11-03T20:31:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67750437496522904257717123986902259107","date":"2025-11-01T13:29:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58417394403338053905786687874308501078","date":"2025-10-31T16:10:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T17:29:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116570847662683082041629198318583201653","date":"2025-10-05T04:11:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T03:32:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-28T00:56:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T17:13:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2025-09-16T12:26:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba49a1fe-18e8-4091-9c22-0f2d77fbe239","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56227392,"name":"Health sciences/Cardiology"},{"id":56227393,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56227394,"name":"Physical sciences/Engineering"},{"id":56227395,"name":"Health sciences/Health care"},{"id":56227396,"name":"Physical sciences/Mathematics and computing"},{"id":56227397,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-16T16:04:21+00:00","versionOfRecord":{"articleIdentity":"rs-7630488","link":"https://doi.org/10.1038/s41746-026-02381-3","journal":{"identity":"npj-digital-medicine","isVorOnly":false,"title":"npj Digital Medicine"},"publishedOn":"2026-03-10 16:00:10","publishedOnDateReadable":"March 10th, 2026"},"versionCreatedAt":"2025-10-16 19:25:52","video":"","vorDoi":"10.1038/s41746-026-02381-3","vorDoiUrl":"https://doi.org/10.1038/s41746-026-02381-3","workflowStages":[]},"version":"v1","identity":"rs-7630488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7630488","identity":"rs-7630488","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}