AI-driven Adoptive Non-invasive Continuous Glucose Monitoring: Personalized Biosensor for Each Unique Patient

AI-driven Adoptive Non-invasive Continuous Glucose Monitoring: Personalized Biosensor for Each Unique Patient | 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 AI-driven Adoptive Non-invasive Continuous Glucose Monitoring: Personalized Biosensor for Each Unique Patient Xi Xie, Chuanjie Yao, Xinshuo Huang, Xinze Wang, Lukang Gao, HaoLin Wang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7477881/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 Accurate continuous glucose monitoring (CGM) in non-invasive way have remained highly challenging, where current universal approach of “one-sensor-fits all” have been always frustrated, since the correlations between non-invasive parameters and blood glucose (BG) levels are highly individualized due to complex physiological states. For the first time to our knowledge, we proposed the methodology of personalized sensor-based “Adoptive Non-invasive CGM” that could potentially achieve the “holy grail” of accurate CGM in non-invasive way. The device included a short-term used microneedle minimally-invasive CGM (MI-CGM) module to measure BG in interstitial fluid, and a long-term used non-invasive CGM (NI-CGM) module based on metabolic heat conformation. The NI-CGM module of the worn device on body gradually (~ 2 days) learned the individual’s physiological characteristics and the accurate glucose sensing capability from MI-CGM module through GRU-based AI model, ultimately forming personalized sensor tailored for each unique patient. The MI-CGM module was then removed while only the NI-CGM module was remained for longer-term use. The short-term (5-consecutive days) performance of Adoptive Non-invasive CGM was demonstrated on 6 participates, with clinical-acceptable accuracy (MARD ~ 15.4%). This method also presented good reproducibility in parallel testing, and possessed reasonable accuracy (error ~ 20%) during long-term (> 2 months) use. This was likely the first time that non-invasive CGM was demonstrated to meet clinical standards of accuracy (MARD ≈ 15%) in multi-day continuous monitoring, while BG trends also highly match the actual BG curve fluctuation. Our personalized sensors could potentially change the conventionally used strategy of developing CGM to adapt universal patients, and held promise for addressing the “holy grail” problem in CGM field. Physical sciences/Engineering/Biomedical engineering Physical sciences/Engineering/Electrical and electronic engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information 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-7477881","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508867473,"identity":"cc73bc34-4253-466e-b4ee-cf130423e0ad","order_by":0,"name":"Xi 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