DETECTING MICROCEPHALY AND MACROCEPHALY FROM ULTRASOUND IMAGES USING ARTIFICIAL INTELLIGENCE

DETECTING MICROCEPHALY AND MACROCEPHALY FROM ULTRASOUND IMAGES USING ARTIFICIAL INTELLIGENCE | 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 DETECTING MICROCEPHALY AND MACROCEPHALY FROM ULTRASOUND IMAGES USING ARTIFICIAL INTELLIGENCE Abraham Keffale Mengistu, Bayou Tilahun Assaye, Addisu Baye Flatie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5914028/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 May, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 8 You are reading this latest preprint version Abstract Background: Microcephaly and macrocephaly, which are abnormal congenital markers, are associated with developmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imaging early on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access to trained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met. Objective: This study aims to develop a fetal head abnormality detection model from ultrasound images via deep learning. Methods: Data were collected from three Ethiopian healthcare facilities to increase model generalizability. The recruitment period for this study started on November 9, 2024, and ended on November 30, 2024. Several preprocessing techniques, such as augmentation, noise reduction, and normalization, have been performed. SegNet, UNet, FCN, MobileNetV2, and EfficientNet-B0 were applied for segmenting and measuring fetal head structures from ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHO guidelines for gestational age, and then the model performance was compared with that of existing industry experts. The metrics used for evaluation included accuracy, precision, recall, the F1 score, and the Dice coefficient. Results: This study was able to demonstrate the feasibility of using SegNet for automatic segmentation, measurement of abnormalities of the fetal head, and classification of macrocephaly and microcephaly, with an accuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of 92.5% and 91.2% for the BPD and HC measurements, respectively. Conclusion: Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrained settings. Future work needs to be done on optimizing model performance, trying complex models, and expanding datasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery. Microcephaly Macrocephaly Congenital abnormality HC BPD Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 May, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers invited by journal 14 Apr, 2025 Submission checks completed at journal 14 Apr, 2025 First submitted to journal 09 Apr, 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-5914028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442595522,"identity":"d2085d20-1191-45da-b7c4-e745bee397e3","order_by":0,"name":"Abraham Keffale Mengistu","email":"data:image/png;base64,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","orcid":"","institution":"Debre Markos University","correspondingAuthor":true,"prefix":"","firstName":"Abraham","middleName":"Keffale","lastName":"Mengistu","suffix":""},{"id":442595523,"identity":"6d821581-6815-46fa-86e8-bbf1e2092367","order_by":1,"name":"Bayou Tilahun Assaye","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Bayou","middleName":"Tilahun","lastName":"Assaye","suffix":""},{"id":442595524,"identity":"bb9c0e3a-5e39-430b-88ea-ea74004092c4","order_by":2,"name":"Addisu Baye Flatie","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Addisu","middleName":"Baye","lastName":"Flatie","suffix":""},{"id":442595525,"identity":"a872859c-1cb2-4122-8768-4a7ff65cde33","order_by":3,"name":"Zewdie Mossie","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Zewdie","middleName":"","lastName":"Mossie","suffix":""}],"badges":[],"createdAt":"2025-01-27 17:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5914028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5914028/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-025-01709-x","type":"published","date":"2025-05-26T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83783022,"identity":"2e77bdfc-87c3-4d6d-8b3c-1ec5cff33e1f","added_by":"auto","created_at":"2025-06-02 16:10:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":984472,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5914028/v1_covered_4fe33222-f7f9-4dc8-b538-4417ece69cea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DETECTING MICROCEPHALY AND MACROCEPHALY FROM ULTRASOUND IMAGES USING ARTIFICIAL INTELLIGENCE","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":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microcephaly, Macrocephaly, Congenital abnormality, HC, BPD","lastPublishedDoi":"10.21203/rs.3.rs-5914028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5914028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Microcephaly and macrocephaly, which are abnormal congenital markers, are associated with developmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imaging early on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access to trained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aims to develop a fetal head abnormality detection model from ultrasound images via deep learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data were collected from three Ethiopian healthcare facilities to increase model generalizability. The recruitment period for this study started on November 9,\u003csup\u003e \u003c/sup\u003e2024, and ended on November 30, 2024. Several preprocessing techniques, such as augmentation, noise reduction, and normalization, have been performed. SegNet, UNet, FCN, MobileNetV2, and EfficientNet-B0 were applied for segmenting and measuring fetal head structures from ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHO guidelines for gestational age, and then the model performance was compared with that of existing industry experts. The metrics used for evaluation included accuracy, precision, recall, the F1 score, and the Dice coefficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e This study was able to demonstrate the feasibility of using SegNet for automatic segmentation, measurement of abnormalities of the fetal head, and classification of macrocephaly and microcephaly, with an accuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of 92.5% and 91.2% for the BPD and HC measurements, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrained settings. Future work needs to be done on optimizing model performance, trying complex models, and expanding datasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery.\u003c/p\u003e","manuscriptTitle":"DETECTING MICROCEPHALY AND MACROCEPHALY FROM ULTRASOUND IMAGES USING ARTIFICIAL INTELLIGENCE","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 12:28:41","doi":"10.21203/rs.3.rs-5914028/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-28T04:57:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T04:56:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T06:35:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305122800328402131982400622322396727638","date":"2025-04-19T09:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54738268665915750554371801219339474655","date":"2025-04-14T08:57:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-14T08:47:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-14T05:03:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-04-09T07:38:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c25b707-aa83-4712-8184-6a7e76840e38","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:04:40+00:00","versionOfRecord":{"articleIdentity":"rs-5914028","link":"https://doi.org/10.1186/s12880-025-01709-x","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2025-05-26 15:57:19","publishedOnDateReadable":"May 26th, 2025"},"versionCreatedAt":"2025-04-15 12:28:41","video":"","vorDoi":"10.1186/s12880-025-01709-x","vorDoiUrl":"https://doi.org/10.1186/s12880-025-01709-x","workflowStages":[]},"version":"v1","identity":"rs-5914028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5914028","identity":"rs-5914028","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}