{"paper_id":"01d41707-e46a-45b5-9e2f-d67300597e57","body_text":"Real-Time Burned Arm Localization Using Stereo Vision for Clinical Decision Support | 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 Real-Time Burned Arm Localization Using Stereo Vision for Clinical Decision Support Jinhao Wu, Hanyue Mo, Jintao Lu, Xinyi Cai, Yuchen Sun, Ying Fang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6012397/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 Burn assessment constitutes a pivotal component of clinical decision-making, particularly within the domain of global public health.In this study, we propose a real-time burn localization method that utilizes binocular vision and the semi-global block matching (SGBM) algorithm. This method employs three dimensional reconstruction techniques to achieve millimeter-level accuracy in the localization of the burned area. The system utilizes a binocular camera to capture high-quality skin images, employs the Zhang Zhengyou calibration method to calibrate the camera parameters, and utilizes the SGBM algorithm to fuse global and local information for stereo matching. This approach effectively reduces parallax map noise and generates highly accurate depth maps.Subsequently, the burned area was calibrated in the world coordinate system using three dimensional reconstruction technology to generate visualized medical images.These images support the tracking of the treatment process and optimize decision-making. The experimental findings demonstrate that the depth error of the system can be maintained within 2 mm (450-500 mm error less than 1 mm) within the working distance of 450-800 mm, and the processing time of a single frame is 0.42 seconds, which fulfills the real-time requirement. The integration of binocular vision with the SGBM algorithm provides a solution for burn localization that is both accurate and efficient, thus demonstrating its great potential for application in the clinical decision-making process.This advancement is expected to improve the quality of rehabilitation management for burn patients. binocular stereo vision burn diagnosis real-time positioning sgbm algorithm camera calibration deep learning 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-6012397\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":414735345,\"identity\":\"ca2fc9ff-db01-41dc-a303-7ff639853356\",\"order_by\":0,\"name\":\"Jinhao Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"China Jiliang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jinhao\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":414735346,\"identity\":\"9a983553-a3e9-4caa-8752-c25b54b51b84\",\"order_by\":1,\"name\":\"Hanyue 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