dc.contributor.author | Gende, M. | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Robles, Patricia | |
dc.contributor.author | Fernández-Vigo, José Ignacio | |
dc.contributor.author | Martínez-de-la-Casa, José María | |
dc.contributor.author | García-Feijóo, Julián | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-11-28T11:52:35Z | |
dc.date.available | 2024-11-28T11:52:35Z | |
dc.date.issued | 2024-11-19 | |
dc.identifier.citation | M. Gende et al., «Circumpapillary OCT-based multi-sector analysis of retinal layer thickness in patients with glaucoma and high myopia», Computerized Medical Imaging and Graphics, vol. 118, p. 102464, dic. 2024, doi: 10.1016/j.compmedimag.2024.102464. | es_ES |
dc.identifier.issn | 1879-0771 | |
dc.identifier.issn | 0895-6111 | |
dc.identifier.uri | http://hdl.handle.net/2183/40422 | |
dc.description.abstract | [Abstract]: Glaucoma is the leading cause of irreversible blindness worldwide. The diagnosis process for glaucoma involves the measurement of the thickness of retinal layers in order to track its degeneration. The elongated shape of highly myopic eyes can hinder this diagnosis process, since it affects the OCT scanning process, producing deformations that can mimic or mask the degeneration caused by glaucoma. In this work, we present the first comprehensive cross-disease analysis that is focused on the anatomical structures most impacted in glaucoma and high myopia patients, facilitating precise differential diagnosis from those solely afflicted by myopia. To achieve this, a fully automatic approach for the retinal layer segmentation was specifically tailored for the accurate measurement of retinal thickness in both highly myopic and emmetropic eyes. To the best of our knowledge, this is the first approach proposed for the analysis of retinal layers in circumpapillary optical coherence tomography images that takes into account the elongation of the eyes in myopia, thus addressing critical diagnostic needs. The results from this study indicate that the temporal superior (mean difference
11.1 µm, p < 0.05, nasal inferior (13.1 µm, p < 0.01) and temporal inferior (13.3 µm, p < 0.01) sectors of the retinal nerve fibre layer show the most significant reduction in retinal thickness in patients of glaucoma and myopia with regards to patients of myopia. | es_ES |
dc.description.sponsorship | This research was funded by Ministerio de Ciencia e Innovación, Government of Spain [research projects PDC2022-133132-I00,TED2021-131201B-I00, and PID2023-148913OB-I00]; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva [grant number ED431C 2024/33], predoctoral grant [grant number ED481A 2021/161]. This work was supported by the Instituto de Salud Carlos III (ISCIII) under the grant [FORT23/00010] as part of the Programa FORTALECE of Ministerio de Ciencia e Innovación and research project [PI23/00828]: Desarrollo
evaluación de un algoritmo de detección de glaucoma a partir de un abordaje multimodal en pacientes con miopía magna, as well as Thea Research Grant 2022-2024. The funding organisations had no role in the design or conduct of this research. Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2024/33 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A 2021/161 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.compmedimag.2024.102464 | es_ES |
dc.rights | Atribución-NoComercial 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ | * |
dc.subject | Glaucoma | es_ES |
dc.subject | Myopia | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Segmentation | es_ES |
dc.subject | Computer-aided diagnosis | es_ES |
dc.subject | Ophthalmology | es_ES |
dc.title | Circumpapillary OCT-based multi-sector analysis of retinal layer thickness in patients with glaucoma and high myopia | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
UDC.journalTitle | Computerized Medical Imaging and Graphics | es_ES |
UDC.volume | 118 | es_ES |
UDC.startPage | 102464 | es_ES |
UDC.coleccion | Investigación | es_ES |
UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
UDC.grupoInv | Grupo de Visión Artificial e Recoñecemento de Patróns (VARPA) | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/PDC2022-133132-I00/ES/MEJORAS EN EL DIAGNÓSTICO E INVESTIGACIÓN CLÍNICO MEDIANTE TECNOLOGÍAS INTELIGENTES APLICADAS LA IMAGEN OFTALMOLÓGICA | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-131201B-I00/ES/DIAGNÓSTICO DIGITAL: TRANSFORMACIÓN DE LA DETECCIÓN DE ENFERMEDADES NEUROVASCULARES Y DEL TRATAMIENTO DE LOS PACIENTES | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-148913OB-I00/ES/IA CONFIABLE Y EXPLICABLE PARA EL DIAGNÓSTICO POR IMAGEN MÉDICA ASISTIDO POR ORDENADOR: NUEVOS AVANCES Y APLICACIONES | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FORT23%2F00010/ES/SOLICITUD DEL INSTITUTO DE INVESTIGACIÓN BIOMÉDICA DE A CORUÑA (INIBIC) PARA EL PROGRAMA FORTALECE | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PI23%2F00828/ES/DESARROLLO Y EVALUACIÓN DE UN ALGORITMO DE DETECCIÓN DE GLAUCOMA A PARTIR DE UN ABORDAJE MULTIMODAL EN PACIENTES CON MIOPÍA MAGNA | es_ES |