AI-Driven Pain Monitoring Dashboard for Infants: A Pilot Study for NICU and Family Medicine Clinics

AI-Driven Pain Monitoring Dashboard for Infants: A Pilot Study for NICU and Family Medicine Clinics | 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 AI-Driven Pain Monitoring Dashboard for Infants: A Pilot Study for NICU and Family Medicine Clinics Oussama El Othmani, Riadh Ouersighni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7496944/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 Effective pain assessment in infants aged 0–3 months is a critical challenge in neonatal intensive care units (NICUs) and family medicine clinics, where self-reporting is impossible and current observational tools remain subjective and inconsistent. This paper presents a pilot study proposal for an AI-driven pain monitoring dashboard designed to support clinicians and caregivers in real time. The system integrates multimodal inputs, including facial video, cry audio, and optional physiological signals, processed through a deep learning framework with self-supervised pretraining and few-shot adaptation to overcome limited labeled neonatal data. A clinician-friendly dashboard displays continuous pain scores, highlights salient modality contributions, and issues alerts during high-risk events. The approach ensures transparency and trust by providing explainability through saliency maps and modality-specific visualizations. This pilot will evaluate the feasibility, usability, and clinical impact of the dashboard in NICU and family clinic environments. The proposed system offers a practical and ethically compliant pathway for deploying explainable AI in early-life pain management, with potential to improve care quality and support medical decision-making. Infant pain monitoring Neonatal intensive care Deep learning Self-supervised learning Multimodal fusion Explainable AI Family medicine 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. 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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-7496944","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511654876,"identity":"b64d74fb-b4bf-4253-b318-4eecfa9c5f3f","order_by":0,"name":"Oussama El Othmani","email":"data:image/png;base64,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","orcid":"","institution":"Military Academy of Fondouk Jedid","correspondingAuthor":true,"prefix":"","firstName":"Oussama","middleName":"El","lastName":"Othmani","suffix":""},{"id":511654877,"identity":"e7abebb5-e21d-4108-b0c9-f97d8e79c1cf","order_by":1,"name":"Riadh Ouersighni","email":"","orcid":"","institution":"Military Academy of Fondouk Jedid","correspondingAuthor":false,"prefix":"","firstName":"Riadh","middleName":"","lastName":"Ouersighni","suffix":""}],"badges":[],"createdAt":"2025-08-30 18:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7496944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7496944/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93450372,"identity":"e8d2b636-5743-468a-9d6c-d04e90989f4b","added_by":"auto","created_at":"2025-10-14 03:16:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1586476,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNaturePaperBRES.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7496944/v1_covered_e6194581-4d48-426e-a30f-45b50215e891.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Driven Pain Monitoring Dashboard for Infants: A Pilot Study for NICU and Family Medicine Clinics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Infant pain monitoring, Neonatal intensive care, Deep learning, Self-supervised learning, Multimodal fusion, Explainable AI, Family medicine","lastPublishedDoi":"10.21203/rs.3.rs-7496944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7496944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Effective pain assessment in infants aged 0–3 months is a critical challenge in neonatal intensive care units (NICUs) and family medicine clinics, where self-reporting is impossible and current observational tools remain subjective and inconsistent. 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