A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development

This 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 keratocon...

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Detalles Bibliográficos
Autores: Bolarín Guillén, José Miguel, Cavas Martínez, Francisco, Velázquez Blázquez, José Sebastián, Alió Sanz, Jorge Luciano
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad Politécnica de Cartagena(UPCT)
Repositorio:Repositorio Digital UPCT
OAI Identifier:oai:repositorio.upct.es:10317/9329
Acceso en línea:http://hdl.handle.net/10317/9329
https://www.mdpi.com/2076-3417/10/5/1874
Access Level:acceso abierto
Palabra clave:Scheimpflug
3D cornea model
Early keratoconus
Corrected Distance Visual Acuity (CDVA)
Expresión Gráfica en Ingeniería
3201.09 Oftalmología
1203.09 Diseño Con Ayuda del Ordenador
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oai_identifier_str oai:repositorio.upct.es:10317/9329
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repository_id_str
spelling A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI developmentBolarín Guillén, José MiguelCavas Martínez, FranciscoVelázquez Blázquez, José SebastiánAlió Sanz, Jorge LucianoScheimpflug3D cornea modelEarly keratoconusCorrected Distance Visual Acuity (CDVA)Expresión Gráfica en Ingeniería3201.09 Oftalmología1203.09 Diseño Con Ayuda del OrdenadorThis 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.This 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).MDPIInstituto de Salud Carlos III202120212020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10317/9329https://www.mdpi.com/2076-3417/10/5/1874reponame:Repositorio Digital UPCTinstname:Universidad Politécnica de Cartagena(UPCT)InglésAnálisis morfogeométrico de la estructura hemiesférica del segmento anterior del ojo humano y su aplicación clínicahttp://hdl.handle.net/10317/9086RD16/0008/0012Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:repositorio.upct.es:10317/93292026-05-15T06:39:02Z
dc.title.none.fl_str_mv A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
title A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
spellingShingle A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
Bolarín Guillén, José Miguel
Scheimpflug
3D cornea model
Early keratoconus
Corrected Distance Visual Acuity (CDVA)
Expresión Gráfica en Ingeniería
3201.09 Oftalmología
1203.09 Diseño Con Ayuda del Ordenador
title_short A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
title_full A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
title_fullStr A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
title_full_unstemmed A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
title_sort A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
dc.creator.none.fl_str_mv Bolarín Guillén, José Miguel
Cavas Martínez, Francisco
Velázquez Blázquez, José Sebastián
Alió Sanz, Jorge Luciano
author Bolarín Guillén, José Miguel
author_facet Bolarín Guillén, José Miguel
Cavas Martínez, Francisco
Velázquez Blázquez, José Sebastián
Alió Sanz, Jorge Luciano
author_role author
author2 Cavas Martínez, Francisco
Velázquez Blázquez, José Sebastián
Alió Sanz, Jorge Luciano
author2_role author
author
author
dc.contributor.none.fl_str_mv Instituto de Salud Carlos III
dc.subject.none.fl_str_mv Scheimpflug
3D cornea model
Early keratoconus
Corrected Distance Visual Acuity (CDVA)
Expresión Gráfica en Ingeniería
3201.09 Oftalmología
1203.09 Diseño Con Ayuda del Ordenador
topic Scheimpflug
3D cornea model
Early keratoconus
Corrected Distance Visual Acuity (CDVA)
Expresión Gráfica en Ingeniería
3201.09 Oftalmología
1203.09 Diseño Con Ayuda del Ordenador
description This 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.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10317/9329
https://www.mdpi.com/2076-3417/10/5/1874
url http://hdl.handle.net/10317/9329
https://www.mdpi.com/2076-3417/10/5/1874
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Análisis morfogeométrico de la estructura hemiesférica del segmento anterior del ojo humano y su aplicación clínica
http://hdl.handle.net/10317/9086
RD16/0008/0012
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositorio Digital UPCT
instname:Universidad Politécnica de Cartagena(UPCT)
instname_str Universidad Politécnica de Cartagena(UPCT)
reponame_str Repositorio Digital UPCT
collection Repositorio Digital UPCT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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