Diagnostic Performance of an Artificial Intelligence Model for Detecting Pediatric Elbow Injuries on Radiographs: A Preliminary Study

Diagnostic Performance of an Artificial Intelligence Model for Detecting Pediatric Elbow Injuries on Radiographs: A Preliminary Study | 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 Diagnostic Performance of an Artificial Intelligence Model for Detecting Pediatric Elbow Injuries on Radiographs: A Preliminary Study Le Roux Viljoen, Bilal Aslan, Rudolph Adriaan Hoffmann, Ella Wesselink, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7077778/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 Introduction This study evaluated the capability of a zero-shot AI model to detect bony and soft tissue abnormalities in pediatric elbow radiographs and assessed whether its diagnostic performance could be comparable to that of clinicians. Methods: In this retrospective cohort study, we extracted 2,700 pediatric elbow radiographs from PACS, of which 2,378 met inclusion criteria. These were split into training (1,902), validation (193), and held-out test (169) sets. The AI model (Zen-NAS with ResNet-50 backbone) was trained to detect the presence or absence of pathology based on radiologist reports, using 13 predefined imaging categories (e.g., fractures, effusions, dislocations). Performance was assessed using sensitivity, specificity, and ROC-AUC for binary classification (pathology vs no pathology). Results: The AI model achieved a sensitivity of 87.19%, specificity of 94.12%, and macro-average accuracy of 81.66%. The area under the ROC curve (AUC) was 0.88, indicating strong discriminative performance. Conclusion: This preliminary study demonstrates that a Zen-NAS-based AI model can reliably detect pediatric elbow abnormalities on AP and lateral radiographs. While further validation is required, this technology may offer clinical value as a diagnostic support tool, particularly in settings where specialist radiology services are limited. 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-7077778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486478919,"identity":"82e9fb66-f7d7-4b5c-8bfe-085809cee955","order_by":0,"name":"Le Roux 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These were split into training (1,902), validation (193), and held-out test (169) sets. The AI model (Zen-NAS with ResNet-50 backbone) was trained to detect the presence or absence of pathology based on radiologist reports, using 13 predefined imaging categories (e.g., fractures, effusions, dislocations). Performance was assessed using sensitivity, specificity, and ROC-AUC for binary classification (pathology vs no pathology).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe AI model achieved a sensitivity of 87.19%, specificity of 94.12%, and macro-average accuracy of 81.66%. The area under the ROC curve (AUC) was 0.88, indicating strong discriminative performance.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThis preliminary study demonstrates that a Zen-NAS-based AI model can reliably detect pediatric elbow abnormalities on AP and lateral radiographs. While further validation is required, this technology may offer clinical value as a diagnostic support tool, particularly in settings where specialist radiology services are limited.\u003c/p\u003e","manuscriptTitle":"Diagnostic Performance of an Artificial Intelligence Model for Detecting Pediatric Elbow Injuries on Radiographs: A Preliminary Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:32:12","doi":"10.21203/rs.3.rs-7077778/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":"c940abe7-24b8-4047-8519-d75e31fc3313","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T10:41:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 13:32:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7077778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7077778","identity":"rs-7077778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}