SlimDy-YOLO: A Lightweight Dynamic Detector for Pediatric Wrist Fracture Detection in X-ray Images

SlimDy-YOLO: A Lightweight Dynamic Detector for Pediatric Wrist Fracture Detection in X-ray Images | 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 SlimDy-YOLO: A Lightweight Dynamic Detector for Pediatric Wrist Fracture Detection in X-ray Images Zijie Yang, Fangru Zhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9067044/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract In pediatric orthopedics, timely and accurate detection of wrist fractures is essential for effective treatment. However, manual interpretation of X-ray radiographs remains difficult due to growth plates that resemble fracture lines, occult fractures with subtle signs, and the need for fast inference in resource-limited clinical settings. To address these challenges, we propose SlimDy-YOLO, a lightweight dynamic YOLOv11-based detection framework. The framework introduces D-HGNet, a dynamic backbone that combines an efficient PPHGNetV2-inspired design with dynamic convolution for improved fine-grained feature extraction. This improves discrimination between actual fractures and growth plates and increases sensitivity to occult fractures with subtle signs. To further improve efficiency, SlimDy-YOLO adopts Slim-Neck to reduce model complexity during multi-scale feature fusion, and incorporates DySample to better preserve subtle fracture cues during upsampling. On the GRAZPEDWRI-DX dataset, SlimDy-YOLO achieves 68.47% mAP$_{50}$ with only 40.5 GFLOPs and 16.83M parameters, while achieving 265.96 FPS for real-time inference. These results demonstrate that SlimDy-YOLO outperforms representative real-time baselines, achieving a superior balance between accuracy and efficiency, thus offering a promising solution for automated screening in resource-constrained clinical settings. Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research pediatric wrist fracture X-ray imaging YOLOv11 lightweight model dynamic convolution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 13 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 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-9067044","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621589299,"identity":"314bb350-cf6c-4c4b-9828-1bb9d2b7a6a9","order_by":0,"name":"Zijie Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3RMWvCQBTA8RcC6XJy6wsp9iucCC4V/CoeQlxCKfQLJATOJdn1W7RL63jhgdOBa8dIIVOHdikE2tIIHU3UzeH+44Mf73EHYLNdYJwnsf5g2Pdu3jb/s2k38ZeU7JbX4yGHMDyNCD1Ph2wcSj+ORqcR0IVCFtFUaPNVfq4J+FUkoF63CydNFKKhO1HkL4OVIfCzd+Hkpp240GwZZPQgqPcc9BSBeI2E66h24oFUKH9JPm5YFfw0ZHKMMJCp0CyUq4x5gbPfgkcIYpHsYtY8MnojP1dzhqa6L/IOMtkuSvrefyW6Fdbqts8Xs6ey7iAHLm3SZwCbzWazHegPV5tWPLFmVWQAAAAASUVORK5CYII=","orcid":"","institution":"Henan University","correspondingAuthor":true,"prefix":"","firstName":"Zijie","middleName":"","lastName":"Yang","suffix":""},{"id":621589300,"identity":"842e5eac-af23-428c-8d4e-77b9cab9cbc4","order_by":1,"name":"Fangru Zhi","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Fangru","middleName":"","lastName":"Zhi","suffix":""}],"badges":[],"createdAt":"2026-03-09 00:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9067044/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9067044/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106959881,"identity":"f46c762c-14b8-45b5-ade5-baf148d2ea69","added_by":"auto","created_at":"2026-04-15 09:16:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4048680,"visible":true,"origin":"","legend":"","description":"","filename":"SlimDyYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9067044/v1_covered_cb875573-7a8e-4054-b899-0122316744a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SlimDy-YOLO: A Lightweight Dynamic Detector for Pediatric Wrist Fracture Detection in X-ray Images","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":"pediatric wrist fracture; X-ray imaging; YOLOv11; lightweight model; dynamic convolution","lastPublishedDoi":"10.21203/rs.3.rs-9067044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9067044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In pediatric orthopedics, timely and accurate detection of wrist fractures is essential for effective treatment. However, manual interpretation of X-ray radiographs remains difficult due to growth plates that resemble fracture lines, occult fractures with subtle signs, and the need for fast inference in resource-limited clinical settings. To address these challenges, we propose SlimDy-YOLO, a lightweight dynamic YOLOv11-based detection framework. The framework introduces D-HGNet, a dynamic backbone that combines an efficient PPHGNetV2-inspired design with dynamic convolution for improved fine-grained feature extraction. This improves discrimination between actual fractures and growth plates and increases sensitivity to occult fractures with subtle signs. To further improve efficiency, SlimDy-YOLO adopts Slim-Neck to reduce model complexity during multi-scale feature fusion, and incorporates DySample to better preserve subtle fracture cues during upsampling. On the GRAZPEDWRI-DX dataset, SlimDy-YOLO achieves 68.47\\% mAP$_{50}$ with only 40.5 GFLOPs and 16.83M parameters, while achieving 265.96 FPS for real-time inference. 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