[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127820-en":3,"doc-seo-127820-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127820,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Radial polarisation patterns identify macular damage - a machine learning approach","Polarisation-modulated patterns offer a potential route for detecting and monitoring macular damage, including foveolar involvement. This study evaluates their effectiveness using feature selection, Naïve Bayes supervised machine learning, cross-validation, and an interpretable nomogram. A cross-sectional dataset of 520 eyes contrasts normal and abnormal cases assessed with optical coherence tomography, combining polarisation-modulated geometric and optotype patterns with visual function measures. Radially structured polarisation patterns plus age best predict macular damage and foveolar involvement.","Clinical and  \nExperimental Optometry  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tceo20)[www.tandfonline.com/journals/tceo20](homepage: www.tandfonline.com/journals/tceo20)  \nRadial polarisation patterns identify macular damage: a machine learning approach  \nGary P Misson, Stephen J Anderson & Mark C M Dunne  \nTo cite this article: Gary P Misson, Stephen J Anderson & Mark C M Dunne (07 Oct 2024): Radial polarisation patterns identify macular damage: a machine learning approach, Clinical  \nand Experimental Optometry, DOI: 10.1080/08164622.2024.2410890  \nTo link to this article: [https://doi.org/10.1080/08164622.2024.2410890](https://doi.org/10.1080/08164622.2024.2410890)  \n© 2024 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  Published online: 07 Oct 2024. |\n| --- |\n|  Submit your article to this journal  |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tceo20](https://www.tandfonline.com/action/journalInformation?journalCode=tceo20)  \nRESEARCH  \nRadial polarisation patterns identify macular damage: a machine learning approach  \nGary P Misson , Stephen J Anderson  and Mark C M Dunne   \nSchool of Optometry, Aston University, Birmingham, UK  \nABSTRACT  \nClinical relevance: Identifying polarisation-modulated patterns may be an effective method for both detecting and monitoring macular damage.  \nBackground: The aim of this work is to determine the effectiveness of polarisation-modulated patterns in identifying macular damage and foveolar involvement using a methodology that involved feature selection, Naïve Bayes supervised machine learning, cross validation, and use of an interpretable nomogram.  \nMethods: A cross-sectional study involving 520 eyes was undertaken, encompassing both normal and abnormal cases, including those with age-related macular disease, diabetic retinopathy orepiretinal membrane. Macular damage and foveolar integrity were assessed using optical coherence tomography. Various polarisation-modulated geometrical and optotype patterns were employed, along with traditional methods for visual function measurement, to complete perceptual detection and identification measures. Other variables assessed included age, sex, eye (right, left) and ocular media (normal, pseudophakic, cataract) . Redundant variables were removed using a Fast CorrelationBased Filter. The area under the receiver operating characteristic curve and Matthews correlation coefficient were calculated, following 5-fold stratified cross validation, for Naïve Bayes models describing the relationship between the selected predictors of macular damage and foveolar involvement.  \nResults: Only radially structured polarisation-modulated patterns and age emerged as predictors of macular damage and foveolar involvement. All other variables, including traditional logMAR measures of visual acuity, were identified as redundant. Naïve Bayes, utilising the Fast Correlation-Based Filter selected features, provided a good prediction for macular damage and foveolar involvement, with an area under the receiver operating curve exceeding 0.7. Additionally, Matthews correlation coefficient showed a medium size effect for both conditions.  \nConclusions: Radially structured polarisation-modulated geometric patterns outperform polarisation-modulated optotypes and standard logMAR acuity measures in predicting macular damage, regardless of foveolar involvement.  \nARTICLE HISTORY  \nReceived 29 March 2024 Revised 22 September 2024 Accepted 24 September 2024  \nKEYWORDS  \nFCBF feature selection; machine learning; macular disease; naïve bayes; polarisation pattern perception  \nIntroduction  \nPolarised light perception is a well-documented phenomenon in various animal groups, including insects, crustaceans, fish and birds.1 This ability enhances their understanding of th","cbCaipMwuhcV9fTW","https://ap.wps.com/l/cbCaipMwuhcV9fTW","pdf",2163882,2,1,9,"English","en",105,"# Abstract\n# Introduction\n## Polarised light perception in animals and humans\n## Limits of Haidinger’s brushes and advances with non-uniform fields\n## Proposed mechanisms in the foveal retina","[{\"question\":\"What is the main aim of the machine learning approach in this study?\",\"answer\":\"To determine how effective polarisation-modulated patterns are at identifying macular damage and assessing foveolar involvement, using feature selection, Naïve Bayes models, cross-validation, and an interpretable nomogram.\"},{\"question\":\"How was macular damage and foveolar integrity assessed?\",\"answer\":\"Macular damage and foveolar integrity were assessed using optical coherence tomography, alongside conventional visual function measurements.\"},{\"question\":\"Which inputs were most predictive of macular damage and foveolar involvement?\",\"answer\":\"Only radially structured polarisation-modulated patterns and age emerged as predictors; other variables, including standard logMAR visual acuity measures, were identified as redundant.\"}]","Radial polarisation patterns identify macular damage - a machine learning approach | PDF",1785942085,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"radial-polarisation-patterns-identify-macular-damage-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/radial-polarisation-patterns-identify-macular-damage-a-machine-learning-approach/127820/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main aim of the machine learning approach in this study?","Question",{"text":76,"@type":77},"To determine how effective polarisation-modulated patterns are at identifying macular damage and assessing foveolar involvement, using feature selection, Naïve Bayes models, cross-validation, and an interpretable nomogram.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was macular damage and foveolar integrity assessed?",{"text":81,"@type":77},"Macular damage and foveolar integrity were assessed using optical coherence tomography, alongside conventional visual function measurements.",{"name":83,"@type":74,"acceptedAnswer":84},"Which inputs were most predictive of macular damage and foveolar involvement?",{"text":85,"@type":77},"Only radially structured polarisation-modulated patterns and age emerged as predictors; 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