[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125027-en":3,"doc-seo-125027-105":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},125027,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Rapid measurement and machine learning classification of colour vision deficiency","Colour vision deficiencies (CVDs) may reflect genetic variations and can act as biomarkers for acquired impairments in neuro-ophthalmic disorders. Traditional colour-vision tests often detect the presence of CVD but fail to quantify its specific type and severity. This study presents FInD (Foraging Interactive Dprime), a rapid, self-administered assessment tool based on signal detection theory and adaptive d-prime computation. It measures detection and discrimination using chromatic blobs in dynamic luminance noise, and classifies CVD subtype and severity through unsupervised machine learning.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nRapid measurement and machine learning classification of colour vision deficiency  \nPermalink  \n[https://escholarship.org/uc/item/9j07k88k](https://escholarship.org/uc/item/9j07k88k)  \nJournal  \nOphthalmic & Physiological Optics, 43(6)  \nISSN  \n0275-5408  \nAuthors  \nHe, Jingyi  \nBex, Peter JSkerswetat, Jan  \nPublication Date  \n2023-11-01  \nDOI  \n10.1111/opo.13210  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Ophthalmic Physiol Opt. Author manuscript; available in PMC 2024 November 01. |\n| --- | --- |\n\nPublished in final edited form as:  \nOphthalmic Physiol Opt. 2023 November ; 43(6): 1379–1390. doi:10.1111/opo.13210 .  \nRapid measurement and machine learning classification of colour vision deficiency  \nJingyi He 1 , Peter J. Bex 1,* , Jan Skerswetat 1  \n1 Department of Psychology, Northeastern University, Boston, Massachusetts. USA  \nAbstract  \nColour vision deficiencies (CVDs) indicate potential genetic variations and can be important biomarkers of acquired impairment in many neuro-ophthalmic diseases. However, CVDs are typically measured with tests which possess high sensitivity for detecting the presence of a CVD but do not quantify its type or severity. In this study, we introduce FInD (Foraging Interactive Dprime); a novel computer-based, generalizable, rapid, self-administered vision assessment tool and apply it to colour vision testing. This signal detection theory-based adaptive paradigm computed test stimulus intensity from d-prime analysis. Stimuli were chromatic gaussian blobs in dynamic luminance noise, and participants clicked on cells that contained chromatic blobs (detection) or blob pairs of differing colours (discrimination) . Sensitivity and repeatability of FInD colour tasks were compared against the Hardy-Rand-Rittler and the Farnsworth-Munsell 100 hue tests in 19 colour-normal and 18 inherited colour-atypical, age-matched observers. Rayleigh colour match was also completed. Detection and discrimination thresholds were higher for atypical than for typical observers, with selective threshold elevations corresponding to unique CVD types.  \nClassifications ofCVD type and severity via unsupervised machine learning confirmed functional subtypes. FInD tasks reliably detect inherited CVDs, and may serve as valuable tools in basic and clinical colour vision science.  \nKeywords  \ncolour detection; colour discrimination; colour vision deficiency; cone-isolating directions; Kmeans clustering; unsupervised machine learning; vision diagnostics  \nIntroduction  \nConventional phenotypical categories for inherited colour vision deficiency (CVD) are anomalous trichromats (AT), dichromats and monochromacy, with mild to strong colour vision defects, respectively. They can be further referred to as protan, deutan or tritan types, with L-, M-and S-cone relevant deficiencies, respectively.1 Identification and diagnosis of CVDs are critical in many respects. In clinical applications, abnormal colour vision may reveal a hereditary CVD, or an acquired CVD which could signify neural pathway or  \n*[Corresponding author: p.bex@northeastern.edu](Corresponding author: p.bex@northeastern.edu).  \nCompeting interests  \nFInD is patented & owned by Northeastern University, USA. JS & PJB are founders of PerZeption Inc., to which the FInD method is exclusively licensed. JH declares that no competing interests exist.  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \nHe et al. Page 2  \nsystemic disease.2 How","cbCaia2yeVP3kPu4","https://ap.wps.com/l/cbCaia2yeVP3kPu4","pdf",1860838,1,20,"English","en",105,"# Introduction\n## Conventional categories and clinical importance of CVD\n## Limitations of existing colour vision tests\n# Methods\n## FInD adaptive paradigm and stimulus design\n## Participant groups and reference tests\n# Results\n## Threshold differences by typical vs atypical observers\n## Unsupervised machine learning classification of subtypes\n# Discussion\n## Diagnostic utility and clinical relevance","[{\"question\":\"What problem does the study address in existing colour vision testing?\",\"answer\":\"Existing tests are sensitive for detecting CVD presence but typically do not quantify CVD type or severity, and they may require long administration or trained supervision.\"},{\"question\":\"How does FInD measure colour vision deficiency in this work?\",\"answer\":\"FInD is a computer-based, self-administered tool that uses an adaptive signal-detection paradigm to compute stimulus intensity via d-prime analysis, with chromatic blob detection and discrimination tasks.\"},{\"question\":\"What role does machine learning play in the classification results?\",\"answer\":\"Unsupervised machine learning is used to confirm functional subtypes by classifying CVD type and severity based on the measured FInD performance.\"}]","Rapid measurement and machine learning classification of colour vision deficiency | 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problem does the study address in existing colour vision testing?","Question",{"text":75,"@type":76},"Existing tests are sensitive for detecting CVD presence but typically do not quantify CVD type or severity, and they may require long administration or trained supervision.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does FInD measure colour vision deficiency in this work?",{"text":80,"@type":76},"FInD is a computer-based, self-administered tool that uses an adaptive signal-detection paradigm to compute stimulus intensity via d-prime analysis, with chromatic blob detection and discrimination tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does machine learning play in the classification results?",{"text":84,"@type":76},"Unsupervised machine learning is used to confirm functional subtypes by classifying CVD type and severity based on the measured FInD 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