New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
DOI: | 10.1097/j.jcrs.0000000000000946 |
Texto Completo: | http://dx.doi.org/10.1097/j.jcrs.0000000000000946 http://hdl.handle.net/11449/247723 |
Resumo: | Purpose: To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas. Setting: 2 private Brazilian ophthalmological centers. Design: Multicenter case-control study. Methods: This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set. Results:2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P <.0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P =.021). Conclusions: BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC. |
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New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneasPurpose: To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas. Setting: 2 private Brazilian ophthalmological centers. Design: Multicenter case-control study. Methods: This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set. Results:2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P <.0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P =.021). Conclusions: BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC.Faculty of Medicine of São José do Rio Preto, São José do Rio PretoBase Hospital of São José do Rio Preto, São José do Rio PretoVisum Eye Center, São José do Rio PretoDepartment of Computer Science and Statistics Institute of Biosciences Letters and Exact Sciences São Paulo State University at São José do Rio PretoRio Claro Eye Institute, Rio ClaroDepartment of Civil Engineering and Industrial Design School of Engineering University of LiverpoolDepartment of Ophthalmology Federal University of São PauloComputing Institute Federal University of AlagoasDepartment of Ophthalmology Federal University the State of Rio de JaneiroDepartment of Computer Science and Statistics Institute of Biosciences Letters and Exact Sciences São Paulo State University at São José do Rio PretoFaculty of Medicine of São José do Rio PretoBase Hospital of São José do Rio PretoVisum Eye CenterUniversidade Estadual Paulista (UNESP)Rio Claro Eye InstituteUniversity of LiverpoolUniversidade de São Paulo (USP)Federal University of AlagoasFederal University the State of Rio de JaneiroAlmeida, Gildásio CastelloGuido, Rodrigo Capobianco [UNESP]Balarin Silva, Henrique MonteiroBrandão, Cinara CássiaDe Mattos, Luiz CarlosLopes, Bernardo T.Machado, Aydano PamponetAmbrósio, Renato2023-07-29T13:24:01Z2023-07-29T13:24:01Z2022-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1168-1174http://dx.doi.org/10.1097/j.jcrs.0000000000000946Journal of Cataract and Refractive Surgery, v. 48, n. 10, p. 1168-1174, 2022.1873-45020886-3350http://hdl.handle.net/11449/24772310.1097/j.jcrs.00000000000009462-s2.0-85139535442Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Cataract and Refractive Surgeryinfo:eu-repo/semantics/openAccess2023-07-29T13:24:01Zoai:repositorio.unesp.br:11449/247723Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:58:07.586393Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
title |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
spellingShingle |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas Almeida, Gildásio Castello Almeida, Gildásio Castello |
title_short |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
title_full |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
title_fullStr |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
title_full_unstemmed |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
title_sort |
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas |
author |
Almeida, Gildásio Castello |
author_facet |
Almeida, Gildásio Castello Almeida, Gildásio Castello Guido, Rodrigo Capobianco [UNESP] Balarin Silva, Henrique Monteiro Brandão, Cinara Cássia De Mattos, Luiz Carlos Lopes, Bernardo T. Machado, Aydano Pamponet Ambrósio, Renato Guido, Rodrigo Capobianco [UNESP] Balarin Silva, Henrique Monteiro Brandão, Cinara Cássia De Mattos, Luiz Carlos Lopes, Bernardo T. Machado, Aydano Pamponet Ambrósio, Renato |
author_role |
author |
author2 |
Guido, Rodrigo Capobianco [UNESP] Balarin Silva, Henrique Monteiro Brandão, Cinara Cássia De Mattos, Luiz Carlos Lopes, Bernardo T. Machado, Aydano Pamponet Ambrósio, Renato |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Faculty of Medicine of São José do Rio Preto Base Hospital of São José do Rio Preto Visum Eye Center Universidade Estadual Paulista (UNESP) Rio Claro Eye Institute University of Liverpool Universidade de São Paulo (USP) Federal University of Alagoas Federal University the State of Rio de Janeiro |
dc.contributor.author.fl_str_mv |
Almeida, Gildásio Castello Guido, Rodrigo Capobianco [UNESP] Balarin Silva, Henrique Monteiro Brandão, Cinara Cássia De Mattos, Luiz Carlos Lopes, Bernardo T. Machado, Aydano Pamponet Ambrósio, Renato |
description |
Purpose: To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas. Setting: 2 private Brazilian ophthalmological centers. Design: Multicenter case-control study. Methods: This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set. Results:2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P <.0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P =.021). Conclusions: BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-01 2023-07-29T13:24:01Z 2023-07-29T13:24:01Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1097/j.jcrs.0000000000000946 Journal of Cataract and Refractive Surgery, v. 48, n. 10, p. 1168-1174, 2022. 1873-4502 0886-3350 http://hdl.handle.net/11449/247723 10.1097/j.jcrs.0000000000000946 2-s2.0-85139535442 |
url |
http://dx.doi.org/10.1097/j.jcrs.0000000000000946 http://hdl.handle.net/11449/247723 |
identifier_str_mv |
Journal of Cataract and Refractive Surgery, v. 48, n. 10, p. 1168-1174, 2022. 1873-4502 0886-3350 10.1097/j.jcrs.0000000000000946 2-s2.0-85139535442 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Cataract and Refractive Surgery |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1168-1174 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1822182326558261248 |
dc.identifier.doi.none.fl_str_mv |
10.1097/j.jcrs.0000000000000946 |