SurgiFusionAI: Multi-Source Feature Fusion forEvaluating Post-Surgical Ocular Inflammation in PRKand LASIK

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SurgiFusionAI: Multi-Source Feature Fusion forEvaluating Post-Surgical Ocular Inflammation in PRKand LASIK | 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 SurgiFusionAI: Multi-Source Feature Fusion forEvaluating Post-Surgical Ocular Inflammation in PRKand LASIK Zeyu Yan, Yan Li, Zhichen Guo, Haibo Yan, Shiyang Niu, Ouyang Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7516057/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 Within the realm of computational medicine and intelligent health systems, post-surgical ocular inflammation presents acritical challenge for precision recovery optimization. As refractive surgeries like surface ablation and stromal reshapingcontinue to evolve, the capacity to predict and regulate inflammatory outcomes has become central to enhancing therapeuticstrategies—an imperative well-aligned with the aims of adaptive human-centric computing and intelligent modeling outlinedby the targeted special issue. Prior works often depend on oversimplified diffusion-based models or static representationsof cytokine behavior, insufficiently addressing the heterogeneity introduced by varied procedural geometries or temporallydynamic cellular feedback mechanisms. These limitations hinder actionable insight into spatiotemporal immune cascadesand prevent effective modulation across patient profiles. In response, this work introduces a hybrid multi-scale frameworkthat unites biophysical modeling, systems immunology, and precision control theory to simulate and direct inflammatorytrajectories. The methodology integrates the Multiscale Inflammatory Dynamics Encoder (MIDE), which formulates molecular,cellular, and mechanical field interactions via structured operator dynamics; and the Feedback-Optimized ResolutionTrajectory Architecture (FORTA), which adaptively minimizes inflammation through pharmacological field optimizationbased on predictive state deviation mapping. MIDE encodes surgical-specific geometry and strain conditions throughparameterized wound potentials and matrix remodeling profiles, while FORTA dynamically regulates intervention fields viabiologically constrained minimization objectives. Together, these modules provide high-fidelity, procedure-aware predictionsand control strategies that maintain inflammation within therapeutically favorable bounds. Empirical simulations validate theframework’s ability to differentially track and resolve inflammatory profiles associated with distinct surgical approaches, underscoring its alignment with the goals of integrating human-aware modeling and digital health technologies into clinical practice. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Computational Immunology Multi-Scale Modeling Intelligent Therapeutic Control Ocular Biomechanics Post- Operative Regulation 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-7516057","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":527037266,"identity":"3b9d6613-62bd-4400-a63d-beaffd76f578","order_by":0,"name":"Zeyu Yan","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zeyu","middleName":"","lastName":"Yan","suffix":""},{"id":527037267,"identity":"ee90ea43-270c-418f-97f2-74179c938f42","order_by":1,"name":"Yan Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical 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