A Systematic Review of Deep Learning Methods Applied to Ocular Images
Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology is not 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 pote...
- Autores:
-
Perdomo Charry, Oscar Julián
González, Fabio Augusto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/3326
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/3326
https://repositorio.escuelaing.edu.co
- Palabra clave:
- Aprendizaje automático (Inteligencia artificial)
Machine Learning (Artificial Intelligence)
Diagnóstico por imagen
Diagnostic imaging
Ojos - Enfermedades
Eye - Diseases
Hallazgos clínicos
Enfermedades oculares
Bases de datos oculares
Aprendizaje profundo
Diagnóstico clínico
Clinical signs
Ocular diseases
ocular datase
Deep learning
Clinical diagnosis
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Repositorio Institucional ECI |
repository_id_str |
|
dc.title.eng.fl_str_mv |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
dc.title.alternative.spa.fl_str_mv |
Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares |
title |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
spellingShingle |
A Systematic Review of Deep Learning Methods Applied to Ocular Images Aprendizaje automático (Inteligencia artificial) Machine Learning (Artificial Intelligence) Diagnóstico por imagen Diagnostic imaging Ojos - Enfermedades Eye - Diseases Hallazgos clínicos Enfermedades oculares Bases de datos oculares Aprendizaje profundo Diagnóstico clínico Clinical signs Ocular diseases ocular datase Deep learning Clinical diagnosis |
title_short |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
title_full |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
title_fullStr |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
title_full_unstemmed |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
title_sort |
A Systematic Review of Deep Learning Methods Applied to Ocular Images |
dc.creator.fl_str_mv |
Perdomo Charry, Oscar Julián González, Fabio Augusto |
dc.contributor.author.none.fl_str_mv |
Perdomo Charry, Oscar Julián González, Fabio Augusto |
dc.contributor.researchgroup.spa.fl_str_mv |
GiBiome |
dc.subject.armarc.none.fl_str_mv |
Aprendizaje automático (Inteligencia artificial) Machine Learning (Artificial Intelligence) Diagnóstico por imagen Diagnostic imaging Ojos - Enfermedades Eye - Diseases |
topic |
Aprendizaje automático (Inteligencia artificial) Machine Learning (Artificial Intelligence) Diagnóstico por imagen Diagnostic imaging Ojos - Enfermedades Eye - Diseases Hallazgos clínicos Enfermedades oculares Bases de datos oculares Aprendizaje profundo Diagnóstico clínico Clinical signs Ocular diseases ocular datase Deep learning Clinical diagnosis |
dc.subject.proposal.spa.fl_str_mv |
Hallazgos clínicos Enfermedades oculares Bases de datos oculares Aprendizaje profundo Diagnóstico clínico |
dc.subject.proposal.eng.fl_str_mv |
Clinical signs Ocular diseases ocular datase Deep learning Clinical diagnosis |
description |
Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology is not 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 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 the segmentation of part of the eyes, clinical signs, and ocular diagnoses performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivery of interpretable clinical 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 diagnosis |
publishDate |
2020 |
dc.date.issued.none.fl_str_mv |
2020 |
dc.date.accessioned.none.fl_str_mv |
2024-10-16T21:19:12Z |
dc.date.available.none.fl_str_mv |
2024-10-16T21:19:12Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
format |
http://purl.org/coar/resource_type/c_6501 |
status_str |
publishedVersion |
dc.identifier.issn.spa.fl_str_mv |
0124-8170 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.escuelaing.edu.co/handle/001/3326 |
dc.identifier.eissn.spa.fl_str_mv |
: 1909-7735 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Escuela Colombiana de Ingeniería Julio Garavito |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Digital |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.escuelaing.edu.co |
identifier_str_mv |
0124-8170 : 1909-7735 Universidad Escuela Colombiana de Ingeniería Julio Garavito Repositorio Digital |
url |
https://repositorio.escuelaing.edu.co/handle/001/3326 https://repositorio.escuelaing.edu.co |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationedition.spa.fl_str_mv |
Vol. 30 No. 1 Enero - Julio 2020 |
dc.relation.citationendpage.spa.fl_str_mv |
26 |
dc.relation.citationissue.spa.fl_str_mv |
1 |
dc.relation.citationstartpage.spa.fl_str_mv |
9 |
dc.relation.citationvolume.spa.fl_str_mv |
30 |
dc.relation.ispartofjournal.eng.fl_str_mv |
Ciencia e Ingeniería Neogranadina |
dc.relation.references.spa.fl_str_mv |
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Perdomo Charry, Oscar Julián0257d02fec95a5e32cb46abe673774b2González, Fabio Augusto563b70ed192cb24ccf388a7df21a5f2bGiBiome2024-10-16T21:19:12Z2024-10-16T21:19:12Z20200124-8170https://repositorio.escuelaing.edu.co/handle/001/3326: 1909-7735Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.coArtificial intelligence is having an important effect on different areas of medicine, and ophthalmology is not 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 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 the segmentation of part of the eyes, clinical signs, and ocular diagnoses performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivery of interpretable clinical 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 diagnosisLa inteligencia artificial tiene un importante impacto en diversas áreas de la medicina, y 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 impacto potencial en el incremento de pacientes correcta y oportunamente diagnosticados. En oftalmología, los métodos de aprendizaje profundo se han aplicado principalmente en 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 la retinopatía diabética, el glaucoma, la degeneración macular diabética y la 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 han venido rompiendo el estigma de los modelos de caja negra, proveyendo 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 oculares18 páginasapplication/pdfengUniversidad Militar Nueva GranadaBogotá (Colombia)https://revistas.unimilitar.edu.co/index.php/rcin/issue/view/299A Systematic Review of Deep Learning Methods Applied to Ocular ImagesUna revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes ocularesArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 30 No. 1 Enero - Julio 2020261930Ciencia e Ingeniería NeogranadinaA. W. Stitt, N. Lois, R. J. Medina, P. Adamson, and T. M. 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Sun, “Deep Residual Learning for Image Recognition,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778. doi:10.1109/ CVPR.2016.90M. Voets, K. Møllersen, and L. A. Bongo, “Replication Study: Development and Validation of Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. arXiv preprint arXiv:1803.04337. doi:10.1371/journal.pone.0217541.info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAprendizaje automático (Inteligencia artificial)Machine Learning (Artificial Intelligence)Diagnóstico por imagenDiagnostic imagingOjos - EnfermedadesEye - DiseasesHallazgos clínicosEnfermedades ocularesBases de datos ocularesAprendizaje profundoDiagnóstico clínicoClinical signsOcular diseasesocular dataseDeep learningClinical diagnosisTEXTA Systematic Review of Deep Learning Methods Applied to Ocular Images.pdf.txtA Systematic Review of Deep Learning Methods Applied to Ocular Images.pdf.txtExtracted texttext/plain64376https://repositorio.escuelaing.edu.co/bitstream/001/3326/4/A%20Systematic%20Review%20of%20Deep%20Learning%20Methods%20Applied%20to%20Ocular%20Images.pdf.txt1dd2d021374661db4c09f66f20160931MD54metadata only accessTHUMBNAILPortada A Systematic Review of Deep Learning Methods Applied to Ocular Images.PNGPortada A Systematic Review of Deep Learning Methods Applied to Ocular Images.PNGimage/png141308https://repositorio.escuelaing.edu.co/bitstream/001/3326/3/Portada%20A%20Systematic%20Review%20of%20Deep%20Learning%20Methods%20Applied%20to%20Ocular%20Images.PNGd278eef9cd81fad36f7e40ec27f1b6e0MD53open accessA Systematic Review of Deep Learning Methods Applied to Ocular Images.pdf.jpgA Systematic Review of Deep Learning Methods Applied to Ocular Images.pdf.jpgGenerated Thumbnailimage/jpeg10503https://repositorio.escuelaing.edu.co/bitstream/001/3326/5/A%20Systematic%20Review%20of%20Deep%20Learning%20Methods%20Applied%20to%20Ocular%20Images.pdf.jpg9f74e5ec1271f09bf5a5027e7d801752MD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3326/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALA Systematic Review of Deep Learning Methods Applied to Ocular Images.pdfA Systematic Review of Deep Learning Methods Applied to Ocular Images.pdfapplication/pdf884532https://repositorio.escuelaing.edu.co/bitstream/001/3326/1/A%20Systematic%20Review%20of%20Deep%20Learning%20Methods%20Applied%20to%20Ocular%20Images.pdfca60ff8fe32798dad8dba757cde40684MD51metadata only access001/3326oai:repositorio.escuelaing.edu.co:001/33262024-10-17 03:00:56.355metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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 |