Automated Applications for Assessing Abnormal Eyelid Movements: A Systematic Literature Review

Automated Applications for Assessing Abnormal Eyelid Movements: A Systematic Literature Review | 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 Systematic Review Automated Applications for Assessing Abnormal Eyelid Movements: A Systematic Literature Review Gustavo Adolpho Bonesso, Regina Célia Coelho, Carlos Marcelo Gurjão de Godoy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7773892/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 Purpose The primary objective of this study is to identify the technology, methods, and tools commonly employed in automated applications for assessing altered eyelid movements. Methods This review consulted three databases known for their excellent reputation and technical quality: PubMed, Scopus, and Google Scholar. The initial search identified 905 papers. Removing duplicates and applying exclusion criteria narrowed the selection to 31 documents for analysis. The search included publications from January 2018 to June 2024 to focus on recent works. Results This review identified the dominance of image analysis in extracting eyelid-related parameters. The image used to analyze eyelid movements appears in 20 of the 31 selected studies. After image acquisition, machine learning is the predominant technique for extracting eyelid parameters. Recently, mobile applications have facilitated the study of eyelid movements, enabling single-step analyses and broadening accessibility. Blink frequency remains the most utilized parameter for eyelid movement analysis, and cameras are the most common sensor for capturing these movements. Conclusion The analysis identified the most used technology, methods, and tools for analyzing automated applications that assess altered eyelid movements. Specifically, this review shows that image analysis, primarily through machine learning, is vital for evaluating eyelid movements, with blink frequency as the most analyzed parameter. Also, mobile applications have expanded access to these assessments, using cameras as the primary sensor and enabling simpler, single-step analyses. Eyelid movement Machine learning Facial dystonia Dry eye 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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The initial search identified 905 papers. Removing duplicates and applying exclusion criteria narrowed the selection to 31 documents for analysis. The search included publications from January 2018 to June 2024\u003cu\u003e \u003c/u\u003eto focus on recent works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review identified the dominance of image analysis in extracting eyelid-related parameters. The image used to analyze eyelid movements appears in 20 of the 31 selected studies. After image acquisition, machine learning is the predominant technique for extracting eyelid parameters. Recently, mobile applications have facilitated the study of eyelid movements, enabling single-step analyses and broadening accessibility. 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