AI-based support for optical coherence tomography in age-related macular degeneration
Abstract Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OC...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | International Journal of Retina and Vitreous |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40942-024-00549-1 |
| _version_ | 1797209150260772864 |
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| author | Virginia Mares Marcio B. Nehemy Hrvoje Bogunovic Sophie Frank Gregor S. Reiter Ursula Schmidt-Erfurth |
| author_facet | Virginia Mares Marcio B. Nehemy Hrvoje Bogunovic Sophie Frank Gregor S. Reiter Ursula Schmidt-Erfurth |
| author_sort | Virginia Mares |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects. |
| first_indexed | 2024-04-24T09:50:07Z |
| format | Article |
| id | doaj.art-a1b8111bb5a94c2a96baf515cf71d88e |
| institution | Directory Open Access Journal |
| issn | 2056-9920 |
| language | English |
| last_indexed | 2024-04-24T09:50:07Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Retina and Vitreous |
| spelling | doaj.art-a1b8111bb5a94c2a96baf515cf71d88e2024-04-14T11:24:01ZengBMCInternational Journal of Retina and Vitreous2056-99202024-04-0110111110.1186/s40942-024-00549-1AI-based support for optical coherence tomography in age-related macular degenerationVirginia Mares0Marcio B. Nehemy1Hrvoje Bogunovic2Sophie Frank3Gregor S. Reiter4Ursula Schmidt-Erfurth5Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of ViennaDepartment of Ophthalmology, Federal University of Minas GeraisLaboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of ViennaLaboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of ViennaLaboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of ViennaLaboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of ViennaAbstract Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.https://doi.org/10.1186/s40942-024-00549-1Age-related macular degenerationAnti-VEGFArtificial intelligenceChoroidal neovascularizationDeep learningDrusen |
| spellingShingle | Virginia Mares Marcio B. Nehemy Hrvoje Bogunovic Sophie Frank Gregor S. Reiter Ursula Schmidt-Erfurth AI-based support for optical coherence tomography in age-related macular degeneration International Journal of Retina and Vitreous Age-related macular degeneration Anti-VEGF Artificial intelligence Choroidal neovascularization Deep learning Drusen |
| title | AI-based support for optical coherence tomography in age-related macular degeneration |
| title_full | AI-based support for optical coherence tomography in age-related macular degeneration |
| title_fullStr | AI-based support for optical coherence tomography in age-related macular degeneration |
| title_full_unstemmed | AI-based support for optical coherence tomography in age-related macular degeneration |
| title_short | AI-based support for optical coherence tomography in age-related macular degeneration |
| title_sort | ai based support for optical coherence tomography in age related macular degeneration |
| topic | Age-related macular degeneration Anti-VEGF Artificial intelligence Choroidal neovascularization Deep learning Drusen |
| url | https://doi.org/10.1186/s40942-024-00549-1 |
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