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
Summary: | 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 |
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