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 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 gre...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Neogranadina
2019-11-01
|
| Series: | Ciencia e Ingeniería Neogranadina |
| Subjects: | |
| Online Access: | https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4242 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832539819716116480 |
|---|---|
| author | Oscar Julian Perdomo Charry Fabio Augusto González Osorio |
| author_facet | Oscar Julian Perdomo Charry Fabio Augusto González Osorio |
| author_sort | Oscar Julian Perdomo Charry |
| collection | DOAJ |
| description |
Artificial 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 diagnosis
|
| format | Article |
| id | doaj-art-5fe2974675014625878756b072ce8108 |
| institution | Kabale University |
| issn | 0124-8170 1909-7735 |
| language | English |
| publishDate | 2019-11-01 |
| publisher | Editorial Neogranadina |
| record_format | Article |
| series | Ciencia e Ingeniería Neogranadina |
| spelling | doaj-art-5fe2974675014625878756b072ce81082025-02-05T08:57:43ZengEditorial NeogranadinaCiencia e Ingeniería Neogranadina0124-81701909-77352019-11-01301A Systematic Review of Deep Learning Methods Applied to Ocular ImagesOscar Julian Perdomo Charry0https://orcid.org/0000-0001-9493-2324Fabio Augusto González Osorio1https://orcid.org/0000-0001-9009-7288Universidad del RosarioUniversidad Nacional de Colombia Artificial 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 diagnosis https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4242clinical signsocular diseasesocular datasetdeep learningclinical diagnosis |
| spellingShingle | Oscar Julian Perdomo Charry Fabio Augusto González Osorio A Systematic Review of Deep Learning Methods Applied to Ocular Images Ciencia e Ingeniería Neogranadina clinical signs ocular diseases ocular dataset deep learning clinical diagnosis |
| title | 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_short | A Systematic Review of Deep Learning Methods Applied to Ocular Images |
| title_sort | systematic review of deep learning methods applied to ocular images |
| topic | clinical signs ocular diseases ocular dataset deep learning clinical diagnosis |
| url | https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4242 |
| work_keys_str_mv | AT oscarjulianperdomocharry asystematicreviewofdeeplearningmethodsappliedtoocularimages AT fabioaugustogonzalezosorio asystematicreviewofdeeplearningmethodsappliedtoocularimages AT oscarjulianperdomocharry systematicreviewofdeeplearningmethodsappliedtoocularimages AT fabioaugustogonzalezosorio systematicreviewofdeeplearningmethodsappliedtoocularimages |