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dc.contributor.authorAli, Guma
dc.contributor.authorEid, Marwa M.
dc.contributor.authorAhmed, Omar G.
dc.contributor.authorAbotaleb, Mostafa
dc.contributor.authorAlaabdin, Anas M. Zein
dc.contributor.authorBuruga, Bosco Apparatus
dc.date.accessioned2023-10-06T21:11:14Z
dc.date.available2023-10-06T21:11:14Z
dc.date.issued2023-06-17
dc.identifier.citationAli, G., Eid, M.M., Ahmed, O.G., Abotaleb, M., Alaabdin, A.M.Z, Buruga, B. A. (2023). Artificial intelligence in corneal topography: A short article in enhancing eye care. Mesopotamian Journal of Artificial Intelligence in Healthcare, 2023, 31–34. https://doi.org/10.58496/MJAIH/2023/006en_US
dc.identifier.issn3005-365X
dc.identifier.urihttp://dir.muni.ac.ug/xmlui/handle/20.500.12260/574
dc.description.abstractThe eye is a critical part of the human being, as it provides complete vision and the ability to receive and process visual details, and any deficiency in it may affect vision and loss of sight. Corneal topography is one of the essential diagnostic tools in the field of ophthalmology, as it can provide important information about the cornea and the problems that appear in it. Artificial intelligence strategies contribute to the development of the healthcare domain through a group of approaches that have a significant and vital impact on improving the field of ophthalmology. The primary purpose of this paper is to highlight the efficiency of artificial intelligence in extracting features from corneal topography and how these techniques contribute to helping ophthalmologists diagnose corneal topography. Furthermore, the focus is on the performance of AI algorithms, their diagnostic capabilities, and their importance in helping physicians and patients. The effects of this paper confirm the effectiveness and efficiency of artificial intelligence algorithms in the clinical diagnosis of various eye concerns.en_US
dc.language.isoenen_US
dc.publisherMesopotamian Journal of Artificial Intelligence in Healthcareen_US
dc.subjectArtificial intelligenceen_US
dc.subjectCorneal topographyen_US
dc.subjectMachine learningen_US
dc.subjectOphthalmologistsen_US
dc.subjectHealthcareen_US
dc.titleArtificial intelligence in corneal topography: A short article in enhancing eye careen_US
dc.typeArticleen_US


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