Instantaneous self-healing and ultra-low detection limit hydrogel electronic sensor for temporomandibular disorders intelligent diagnosis

Instantaneous self-healing and ultra-low detection limit hydrogel electronic sensor for temporomandibular disorders intelligent diagnosis | 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 Instantaneous self-healing and ultra-low detection limit hydrogel electronic sensor for temporomandibular disorders intelligent diagnosis Hang Zhao, Yujie Yan, Lixia Yu, Xuefeng Zhang, Qi Han, Zhixin Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4582231/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Temporomandibular disorders (TMD) intelligent diagnosis promises to elevate clinical efficiency, facilitating timely TMD management for patients and staving off the emergence of severe TMJ disability and pain. However, development of TMD intelligent diagnostic tools with high accuracy and sensitivity faces challenges, particularly in sensing minute deformations and ensuring rapid self-healing. Here, inspired by clinical joint palpation, based on the minute facial deformations (0.31-1.62 mm) caused by subtle TMJ movements, we report a biocompatible hydrogel electronic sensor that is capable of instantaneous self-healing (within 2.1 s) and ultra-low detection limit (0.005% strain). The clinical trials involving 120 cases demonstrated that it can efficiently identify the typical clinical manifestations of TMD, such as joint clicking, the adjunctive diagnosis of TMD with repeatability (Kappa=0.86) and a satisfactory accuracy of 90.00%, and it also had a clear indication of other uncoordinated TMJ activity and the timing of TMJ clicking, with a sensitivity of up to 100% for the diagnosis of TMD. Furthermore, to mitigate the reliance on physicians for the diagnostic tool, a predictive model based on sensing waveform features was constructed, achieving high accuracy of 84.4% and sensitivity of 0.86. In summary, the hydrogel sensor, to the best of our knowledge, is the first hydrogel-based strain sensor used for TMD monitoring and is expected to become a radiation-free, non-invasive, convenient and efficient examination tool for clinical TMD diagnosis in the future. Health sciences/Diseases/Oral diseases Health sciences/Health care/Dentistry/Oral conditions/Temporomandibular disorders Health sciences/Health care/Diagnosis/Electrodiagnosis Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation5.pdf MovieS1.mp4 Movie S1 MovieS2.mp4 Movie S2 MovieS31.mp4 Movie S3 MovieS41.mp4 Movie S4 MovieS51.mp4 Movie S5 MovieS61.mp4 Movie S6 MovieS71.mp4 Movie S7 MovieS81.mp4 Movie S8 Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Nature Communications → 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-4582231","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":316810086,"identity":"b25864c2-0b2f-408a-8c36-1ce119f86cfe","order_by":0,"name":"Hang 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However, development of TMD intelligent diagnostic tools with high accuracy and sensitivity faces challenges, particularly in sensing minute deformations and ensuring rapid self-healing. Here, inspired by clinical joint palpation, based on the minute facial deformations (0.31-1.62 mm) caused by subtle TMJ movements, we report a biocompatible hydrogel electronic sensor that is capable of instantaneous self-healing (within 2.1 s) and ultra-low detection limit (0.005% strain). The clinical trials involving 120 cases demonstrated that it can efficiently identify the typical clinical manifestations of TMD, such as joint clicking, the adjunctive diagnosis of TMD with repeatability (Kappa=0.86) and a satisfactory accuracy of 90.00%, and it also had a clear indication of other uncoordinated TMJ activity and the timing of TMJ clicking, with a sensitivity of up to 100% for the diagnosis of TMD. Furthermore, to mitigate the reliance on physicians for the diagnostic tool, a predictive model based on sensing waveform features was constructed, achieving high accuracy of 84.4% and sensitivity of 0.86. 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