| Autor: |
R, Bunod, E, Augstburger, E, Brasnu, A, Labbe, C, Baudouin |
| Jazyk: |
francouzština |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Journal francais d'ophtalmologie. 45(2) |
| ISSN: |
1773-0597 |
| Popis: |
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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