Artificial Intelligence-Driven Optimization of Bioresorbable Magnesium-Nanocomposite Vascular Stents for Exact 6-Month Arterial Healing Synchronization

Artificial Intelligence-Driven Optimization of Bioresorbable Magnesium-Nanocomposite Vascular Stents for Exact 6-Month Arterial Healing Synchronization | 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 Artificial Intelligence-Driven Optimization of Bioresorbable Magnesium-Nanocomposite Vascular Stents for Exact 6-Month Arterial Healing Synchronization thirugnanamuthu.N This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9628571/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 Despite the advent of bioresorbable magnesium vascular stents, premature structural failure due to non-uniform degradation and polymer coating delamination remains a significant clinical challenge. This study presents a highly optimized, artificial intelligence-driven computational framework to design a Magnesium-Nanocomposite (Mg-Zn-Ca) stent precisely synchronized with the standard 6-month arterial healing window. A physics-informed dataset of N = 3,000 Finite Element Analysis (FEA) samples was generated to simulate balloon expansion stress and electrochemical mass-loss dynamics. An Artificial Neural Network (ANN) surrogate model (R² = 0.9997) was trained and coupled with a three-stage multi-objective L-BFGS-B optimization algorithm to dynamically tune stent geometry and coating thickness. The baseline Model 1 configuration (strut: 120 µm, coating: 25 µm) yielded a stent lifespan of 8.86 months — a clinically unacceptable overrun of + 2.86 months. The AI-optimized Model 2 parameters (strut: 95.00 µm, coating: 16.00 µm) maintained a peak Von Mises stress of 18.28 MPa against UTS = 200.0 MPa (Safety Factor: 10.94), eliminated coating delamination risk (D_risk = 0.209), and achieved an exact structural degradation timeline of 6.00 months with zero residual days of error. This methodology provides a robust foundation for next-generation, patient-specific bioresorbable cardiovascular implants. Bioresorbable Stents Magnesium Nanocomposite Finite Element Analysis Artificial Neural Networks Biomaterial Degradation Multi-Objective Optimization L-BFGS-B Cardiovascular Implants 6-Month Arterial Healing Full Text Additional Declarations The authors declare no competing interests. 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. 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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-9628571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635389807,"identity":"510f8a9c-eb88-49d3-9bf0-a01051347363","order_by":0,"name":"thirugnanamuthu.N","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABLUlEQVRIiWNgGAWjYFACHoYDSDwwm42B8Z8NkGZsPIBFAwMbipYEEJuZDSicBtLSgEsLAzYthxGWogFz+d6DhyvbavP4Zzc/3cz7407i2hn5xx7z8Jy3W9t+GGhLjU00mhbLNr6Eg2fbjhdL3Dlmdpsn4VnithvJ7MY8EreTt51JBGo5lpbbgKrF4BiPwcHGtmOJDTcSQFoOg7SwSfMY3E42OwDUwthwGKeW+TfSvyFpSTiXbHb+IT4tNYkbbuQg23LggJ3ZDVy25BgcbDh3IHHjjZyym3PSDhtvO/PYTHJuQ3KC2Q2gLQlY/HL4jPHHhrK6xHk30rfdeGNzWHbb8cRnEm8b7OzNzqc/fPChxgZdCxQcxhRKBKtMwKocBOowhexxKh4Fo2AUjIKRBgBje45sFbqhRwAAAABJRU5ErkJggg==","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"thirugnanamuthu.N","suffix":""}],"badges":[],"createdAt":"2026-05-06 09:58:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9628571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9628571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108668731,"identity":"b4334c5e-4f58-473f-8b81-e6cefedd183c","added_by":"auto","created_at":"2026-05-07 07:10:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":408594,"visible":true,"origin":"","legend":"","description":"","filename":"MgStentIEEEPaperv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9628571/v1_covered_6bd99dc2-1473-4b95-a7b3-d66b58eb8709.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eArtificial Intelligence-Driven Optimization of Bioresorbable Magnesium-Nanocomposite Vascular Stents for Exact 6-Month Arterial Healing Synchronization\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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},"keywords":"Bioresorbable Stents, Magnesium Nanocomposite, Finite Element Analysis, Artificial Neural Networks, Biomaterial Degradation, Multi-Objective Optimization, L-BFGS-B, Cardiovascular Implants, 6-Month Arterial Healing","lastPublishedDoi":"10.21203/rs.3.rs-9628571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9628571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the advent of bioresorbable magnesium vascular stents, premature structural failure due to non-uniform degradation and polymer coating delamination remains a significant clinical challenge. This study presents a highly optimized, artificial intelligence-driven computational framework to design a Magnesium-Nanocomposite (Mg-Zn-Ca) stent precisely synchronized with the standard 6-month arterial healing window. A physics-informed dataset of N\u0026thinsp;=\u0026thinsp;3,000 Finite Element Analysis (FEA) samples was generated to simulate balloon expansion stress and electrochemical mass-loss dynamics. An Artificial Neural Network (ANN) surrogate model (R\u0026sup2; = 0.9997) was trained and coupled with a three-stage multi-objective L-BFGS-B optimization algorithm to dynamically tune stent geometry and coating thickness. The baseline Model 1 configuration (strut: 120 \u0026micro;m, coating: 25 \u0026micro;m) yielded a stent lifespan of 8.86 months \u0026mdash; a clinically unacceptable overrun of +\u0026thinsp;2.86 months. The AI-optimized Model 2 parameters (strut: 95.00 \u0026micro;m, coating: 16.00 \u0026micro;m) maintained a peak Von Mises stress of 18.28 MPa against UTS\u0026thinsp;=\u0026thinsp;200.0 MPa (Safety Factor: 10.94), eliminated coating delamination risk (D_risk\u0026thinsp;=\u0026thinsp;0.209), and achieved an exact structural degradation timeline of 6.00 months with zero residual days of error. This methodology provides a robust foundation for next-generation, patient-specific bioresorbable cardiovascular implants.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence-Driven Optimization of Bioresorbable Magnesium-Nanocomposite Vascular Stents for Exact 6-Month Arterial Healing Synchronization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 07:08:25","doi":"10.21203/rs.3.rs-9628571/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":"5b75ddc9-38a4-4125-862b-428bce87d9f7","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T07:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 07:08:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9628571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9628571","identity":"rs-9628571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}