Evaluating Canine Hip Analysis: NANet Model for Norberg Angle Prediction

Evaluating Canine Hip Analysis: NANet Model for Norberg Angle Prediction | 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 Evaluating Canine Hip Analysis: NANet Model for Norberg Angle Prediction Yuxiao Yao, Jialu Li, George Hansen, Sheng-Han Yueh, Rory Todhunter, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8590407/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The Norberg angle (NA) plays a crucial role in evaluating hip joint conformation in canines by quantifying femoral headsubluxation within the hip joint. It is a key metric for assessing hip joint quality and diagnosing canine hip dysplasia (CHD),a multifactorial orthopedic disease influenced by both genetic and environmental factors. While contemporary tools offerautomated NA quantification, they still rely on substantial manual labeling and verification by trained veterinarians. Rather thanfocusing on workflow efficiency, the objective of this study is to develop a fully automated system capable of predicting theNA directly from radiographs, thereby reducing dependence on manual annotation and enabling consistent and reproducibleassessment. To address the limited availability of diverse, high-quality annotated radiographs, our previous work incorporated diffusion-basedgenerative models to augment the training dataset from 1,168 images to 2,693 images. Training with this expanded datasetresulted in substantial performance gains, with an average improvement of 35.3% across evaluation metrics. Our proposedNANet model outperformed state-of-the-art alternatives, demonstrating superior predictive accuracy. In particular, the predictedNorberg angle fell within 5 degrees of the reference measurement in 81.30% of test cases, approaching or surpassing theconsistency observed among human annotators. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 19 Jan, 2026 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. 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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-8590407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578151134,"identity":"10cc8755-21e6-49ba-9884-2218bf5f781b","order_by":0,"name":"Yuxiao 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Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8590407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8590407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Norberg angle (NA) plays a crucial role in evaluating hip joint conformation in canines by quantifying femoral headsubluxation within the hip joint. It is a key metric for assessing hip joint quality and diagnosing canine hip dysplasia (CHD),a multifactorial orthopedic disease influenced by both genetic and environmental factors. While contemporary tools offerautomated NA quantification, they still rely on substantial manual labeling and verification by trained veterinarians. Rather thanfocusing on workflow efficiency, the objective of this study is to develop a fully automated system capable of predicting theNA directly from radiographs, thereby reducing dependence on manual annotation and enabling consistent and reproducibleassessment.\u003c/p\u003e\n\u003cp\u003eTo address the limited availability of diverse, high-quality annotated radiographs, our previous work incorporated diffusion-basedgenerative models to augment the training dataset from 1,168 images to 2,693 images. Training with this expanded datasetresulted in substantial performance gains, with an average improvement of 35.3% across evaluation metrics. Our proposedNANet model outperformed state-of-the-art alternatives, demonstrating superior predictive accuracy. In particular, the predictedNorberg angle fell within 5 degrees of the reference measurement in 81.30% of test cases, approaching or surpassing theconsistency observed among human annotators.\u003c/p\u003e","manuscriptTitle":"Evaluating Canine Hip Analysis: NANet Model for Norberg Angle Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 06:12:24","doi":"10.21203/rs.3.rs-8590407/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"57835591791076026216701346685125144871","date":"2026-05-18T06:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325514558844940740813823622470590370473","date":"2026-05-14T10:31:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T20:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136651735023864230161167649988614752318","date":"2026-04-14T07:31:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153974358214182588815564563539334265867","date":"2026-02-02T11:05:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T10:12:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T09:15:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T05:48:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-19T05:45:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5de226c-54a3-464b-8ac0-c7175fdd40b0","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"57835591791076026216701346685125144871","date":"2026-05-18T06:37:57+00:00","index":74,"fulltext":""},{"type":"reviewerAgreed","content":"325514558844940740813823622470590370473","date":"2026-05-14T10:31:51+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61514363,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61514364,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-01-23T06:12:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 06:12:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8590407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8590407","identity":"rs-8590407","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}