[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120035-en":3,"doc-seo-120035-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120035,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine learning-based prediction of tear osmolarity for contact lens practice","Purpose: This study applies machine learning techniques to estimate tear osmolarity, a clinically important but difficult-to-measure parameter. Elevated tear osmolarity in contact lens wearers relates to contact lens-induced dry eye, a frequent cause of discomfort that can lead to stopping lens wear. Methods: Regression and classification models predict osmolarity using routine clinical measures. Dataset includes 175 primarily healthy soft lens candidates with symptom questionnaires and ocular surface, tear film, staining, and Meibomian gland metrics. Results and conclusions summarize predictive performance and key features.","Received: 3 October 2023  \nAccepted: 3 March 2024  \nDOI: 10.1111/opo.13302  \nO R I G I N A L AR T I CL E  \nMachine learning-based prediction of tear osmolarity for contact lens practice  \nIzabela K. Garaszczuk1  | Maria Romanos-Ibanez2  | Alejandra Consejo2   \n1Wroclaw University of Science and Technology, Wroclaw, Poland  \n2Aragon Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain  \nCorrespondence  \nAlejandra Consejo, Department of Applied Physics, University of Zaragoza, Zaragoza, Spain.  \n[Email: alejandra.consejo@unizar.es](Email: alejandra.consejo@unizar.es)  \nFunding information  \nCátedra SAMCA de Desarrollo Tecnológico de Aragón, Universidad de Zaragoza (III Premio a la Innovación Multidisciplinar); MCIN/ AEI/10.13039/501100011033 and European Union “NextGenerationEU”/PRTR, Grant/ Award Number: TED2021-130723A-I00  \nAbstract  \nPurpose: This study addressed the utilisation of machine learning techniques to estimate tear osmolarity, a clinically significant yet challenging parameter to measure accurately. Elevated tear osmolarity has been observed in contact lens wearersand is associated with contact lens-induced dry eye, a common cause of discomfort leading to discontinuation of lens wear.  \nMethods: The study explored machine learning, regression and classification techniques to predict tear osmolarity using routine clinical parameters. The data set consisted of 175 participants, primarily healthy subjects eligible for soft contact lens wear. Various clinical assessments were performed, including symptom assessment with the Ocular Surface Disease Index and 5-Item Dry Eye Questionnaire (DEQ-5), tear meniscus height (TMH), tear osmolarity, non-invasive keratometric tear film break-up time (NIKBUT), ocular redness, corneal and conjunctival fluorescein staining and Meibomian glands loss.  \nResults: The results revealed that simple linear regression was insufficient for accurate osmolarity prediction. Instead, more advanced regression models achieved a moderate level of predictive power, explaining approximately 32% of the osmolarity variability. Notably, key predictors for osmolarity included NIKBUT, TMH, ocular redness, Meibomian gland coverage and the DEQ-5 questionnaire. In classification tasks, distinguishing between low (\u003C299 mOsmol/L), medium (300–307 mOsmol/L) and high osmolarity (>308 mOsmol/L) levels yielded an accuracy of approximately 80% . Key parameters for classification were similar to those in regression models, emphasising the importance of NIKBUT, TMH, ocular redness, Meibomian glands coverage and the DEQ-5 questionnaire.  \nConclusions: This study highlights the potential benefits of integrating machine learning into contact lens research and practice. It suggests the clinical utility of assessing Meibomian glands and NIKBUT in contact lens fitting and follow-up visits. Machine learning models can optimise contact lens prescriptions and aid in early detection of conditions like dry eye, ultimately enhancing ocular health and the contact lens wearing experience.  \nK E Y W O R D S  \ncontact lenses, dry eye disease, machine learning, regression and classification techniques, tear osmolarity  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2024 The Authors. Ophthalmic and Physiological Optics published by John Wiley & Sons Ltd on behalf of College of Optometrists.  \n2  ML-BASED PREDICTION OF TEAR OSMOLARITY  \nINTRODUCTION  \nMachine learning methods have seen widespread adoption in various domains, including ophthalmology,1 and yet their application in soft contact lens research remains underutilised. This study addresses the application of machine learning techniques in estimating a clinically important, however costly, and challenging-to-measu","cbCair7s9duy11zR","https://ap.wps.com/l/cbCair7s9duy11zR","pdf",1696446,1,10,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Tear osmolarity and dry eye relevance\n## Contact lens discomfort and tear hyperosmolarity\n## Need for managing tear osmolarity","[{\"question\":\"Why is tear osmolarity important in contact lens practice?\",\"answer\":\"Tear osmolarity reflects the concentration of osmotically active particles in tears and provides an objective indicator for diagnosing and grading dry eye disease. Elevated osmolarity is associated with contact lens-induced dry eye and related discomfort that can cause lens discontinuation.\"},{\"question\":\"What machine learning approaches were used to predict tear osmolarity?\",\"answer\":\"The study evaluated regression and classification methods using routine clinical parameters. Linear regression was insufficient, while more advanced regression models achieved moderate predictive power and classification separated low, medium, and high osmolarity ranges with about 80% accuracy.\"},{\"question\":\"Which clinical parameters were most influential for the models?\",\"answer\":\"Key predictors included NIKBUT and tear meniscus height, along with ocular redness and Meibomian gland coverage, plus the DEQ-5 questionnaire. Similar features supported both regression and classification tasks.\"}]","Machine learning-based prediction of tear osmolarity for contact lens practice | PDF",1785727827,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-prediction-of-tear-osmolarity-for-contact-lens-practice","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-prediction-of-tear-osmolarity-for-contact-lens-practice/120035/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is tear osmolarity important in contact lens practice?","Question",{"text":75,"@type":76},"Tear osmolarity reflects the concentration of osmotically active particles in tears and provides an objective indicator for diagnosing and grading dry eye disease. Elevated osmolarity is associated with contact lens-induced dry eye and related discomfort that can cause lens discontinuation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approaches were used to predict tear osmolarity?",{"text":80,"@type":76},"The study evaluated regression and classification methods using routine clinical parameters. Linear regression was insufficient, while more advanced regression models achieved moderate predictive power and classification separated low, medium, and high osmolarity ranges with about 80% accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"Which clinical parameters were most influential for the models?",{"text":84,"@type":76},"Key predictors included NIKBUT and tear meniscus height, along with ocular redness and Meibomian gland coverage, plus the DEQ-5 questionnaire. 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