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dc.contributor.authorBolarín Guillén, José Miguel 
dc.contributor.authorCavas Martínez, Francisco 
dc.contributor.authorVelázquez Blázquez, José Sebastián 
dc.contributor.authorAlió Sanz, Jorge Luciano 
dc.date.accessioned2021-04-28T10:43:33Z
dc.date.available2021-04-28T10:43:33Z
dc.date.issued2020
dc.identifier.citationBolarín, Jose & Cavas, F. & Velázquez, J.S. & Alió, Jorge. (2020). A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development. Applied Sciences. 10. 1874. 10.3390/app10051874.es_ES
dc.identifier.issn2076-3417
dc.description.abstractThis work pursues two objectives: defining a new concept of risk probability associated with su_ering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Di_erent demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms.es_ES
dc.description.sponsorshipThis publication has been carried out as part of the Thematic Network for Co-Operative Research in Health (RETICS), reference number RD16/0008/0012, financed by the Carlos III Health Institute-General Subdirection of Networks and Cooperative Investigation Centers (R&D&I National Plan 2013-2016), European Regional Development Funds (FEDER), and the Results Valorization Program financed by the Technical University of Cartagena (PROVALOR-UPCT).es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleA machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI developmentes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subjectScheimpfluges_ES
dc.subject3D cornea modeles_ES
dc.subjectEarly keratoconuses_ES
dc.subjectCorrected Distance Visual Acuity (CDVA)es_ES
dc.subject.otherExpresión Gráfica en Ingenieríaes_ES
dc.identifier.urihttp://hdl.handle.net/10317/9329
dc.identifier.doi10.3390/app10051874
dc.identifier.urlhttps://www.mdpi.com/2076-3417/10/5/1874
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.relation.projectIDRD16/0008/0012es_ES
dc.subject.unesco3201.09 Oftalmologíaes_ES
dc.subject.unesco1203.09 Diseño Con Ayuda del Ordenadores_ES
dc.contributor.funderInstituto de Salud Carlos IIIes_ES


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