Novel morphometric features in hand radiographs for diagnostic applications

Novel morphometric features in hand radiographs for diagnostic applications | 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 Novel morphometric features in hand radiographs for diagnostic applications Philipp Schmidt, Eike Bolmer, Alexandra Keller, Julian Nevado, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9012917/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Hand radiographs are routinely used by pediatric endocrinologists to assess bone age in children presenting with atypical growth, pubertal disturbances, or other disorders of physical maturation. However, their diagnostic utility beyond skeletal maturation, particularly for differential diagnosis of growth disorders, remains largely untapped. Here, we introduce novel and well-defined morphometric measurements in hand radiographs and present algorithms for their automatic extraction. We retrospectively analyzed pediatric hand radiographs from 9 conditions (n = 1283), representing a spectrum of growth-affecting endocrine, metabolic, and genetic disorders with variable skeletal involvement (such as SHOX deficiency, Noonan syndrome, and Ullrich-Turner syndrome) and skeletal dysplasias with pronounced hand dysmorphology (such as mucopolysaccharidosis and achondroplasia). From these images, we extracted 550 phenotypical features from metacarpal and phalangeal bones, which were further processed via age- and sex-independent z-scores based on unaffected (UA) controls (n = 1353) to generate a high-dimensional standardized phenotypic space. Using this feature space, a binary screening based solely on the largely available UA training data achieved 82% specificity on an independent UA test set and condition-dependent sensitivities 48.3%-98.9%, enabling robust detection of growth disorders without requiring affected training samples. As a secondary demonstration, a 10-class classifier achieved a balanced test-set accuracy of 75%, with particularly high accuracy for UA controls (94%). Our findings demonstrate that these new measurements provide a low-cost, interpretable, and reproducible method that can be integrated into routine bone age evaluation for growth disorder screening and clinical decision support. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Growth disorders Health sciences/Anatomy/Musculoskeletal system/Bone Full Text Additional Declarations Yes there is potential Competing Interest. P.S., E.B., and B.J. are co-inventors on a pending patent application containing parts of the methods described in this manuscript. Supplementary Files SupplementaryInformation.pdf Supplementary Information to Novel morphometric features in hand radiographs for diagnostic applications Cite Share Download PDF Status: Under Review 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-9012917","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600798534,"identity":"bfc08f66-f308-434b-b86e-61dc94c742d6","order_by":0,"name":"Philipp Schmidt","email":"","orcid":"","institution":"University Hospital Magdeburg, Institute of Human Genetics","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Schmidt","suffix":""},{"id":600798535,"identity":"8459aeaa-366d-4e2e-949a-1d5100e564f2","order_by":1,"name":"Eike Bolmer","email":"","orcid":"","institution":"University Hospital Bonn, Institute for 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However, their diagnostic utility beyond skeletal maturation, particularly for differential diagnosis of growth disorders, remains largely untapped. Here, we introduce novel and well-defined morphometric measurements in hand radiographs and present algorithms for their automatic extraction. We retrospectively analyzed pediatric hand radiographs from 9 conditions (n = 1283), representing a spectrum of growth-affecting endocrine, metabolic, and genetic disorders with variable skeletal involvement (such as SHOX deficiency, Noonan syndrome, and Ullrich-Turner syndrome) and skeletal dysplasias with pronounced hand dysmorphology (such as mucopolysaccharidosis and achondroplasia). From these images, we extracted 550 phenotypical features from metacarpal and phalangeal bones, which were further processed via age- and sex-independent z-scores based on unaffected (UA) controls (n = 1353) to generate a high-dimensional standardized phenotypic space. 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