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Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares

dc.creatorPerdomo Charry, Oscar Julian
dc.creatorGonzález Osorio, Fabio Augusto
dc.date2019-11-12
dc.identifierhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/4242
dc.identifier10.18359/rcin.4242
dc.descriptionArtificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosisen-US
dc.descriptionLa inteligencia artificial está teniendo un importante impacto en diversas áreas de la medicina y a la oftalmología no ha sido la excepción. En particular, los métodos de aprendizaje profundo han sido aplicados con éxito en la detección de signos clínicos y la clasificación de enfermedades oculares. Esto representa un potencial impacto en el incremento de pacientes correctamente y oportunamente diagnosticados. En oftalmología, los métodos de aprendizaje profundo se han aplicado principalmente a imágenes de fondo de ojo y tomografía de coherencia óptica. Por un lado, estos métodos han logrado un rendimiento sobresaliente en la detección de enfermedades oculares tales como: retinopatía diabética, glaucoma, degeneración macular diabética y degeneración macular relacionada con la edad. Por otro lado, varios desafíos mundiales han compartido grandes conjuntos de datos con segmentación de parte de los ojos, signos clínicos y el diagnóstico ocular realizado por expertos. Adicionalmente, estos métodos están rompiendo el estigma de los modelos de caja negra, con la entrega de información clínica interpretable. Esta revisión proporciona una visión general de los métodos de aprendizaje profundo de última generación utilizados en imágenes oftálmicas, bases de datos y posibles desafíos para los diagnósticos oculareses-ES
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dc.formattext/xml
dc.languageeng
dc.publisherUniversidad Militar Nueva Granadaes-ES
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/4242/4078
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/4242/4247
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dc.rightsDerechos de autor 2019 Ciencia e Ingeniería Neogranadinaes-ES
dc.sourceCiencia e Ingenieria Neogranadina; Vol. 30 No. 1 (2020); 9-26en-US
dc.sourceCiencia e Ingeniería Neogranadina; Vol. 30 Núm. 1 (2020); 9-26es-ES
dc.sourceCiencia e Ingeniería Neogranadina; v. 30 n. 1 (2020); 9-26pt-BR
dc.source1909-7735
dc.source0124-8170
dc.subjectclinical signsen-US
dc.subjectocular diseasesen-US
dc.subjectocular dataseten-US
dc.subjectdeep learningen-US
dc.subjectclinical diagnosisen-US
dc.subjecthallazgos clínicoses-ES
dc.subjectenfermedades oculareses-ES
dc.subjectbases de datos oculareses-ES
dc.subjectaprendizaje profundoes-ES
dc.subjectdiagnóstico clínicoes-ES
dc.titleA Systematic Review of Deep Learning Methods Applied to Ocular Imagesen-US
dc.titleUna revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculareses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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