Artificial intelligence and neural networks in the diagnosis of glaucoma
Author:
Affiliation:
1. Municipal Clinical Hospital No. 2, Polyclinic No. 1
2. Central City Hospital
3. Hospital for War Veterans No. 2; P.V. Mandryka Central Military Clinical Hospital
4. Krasnov Research Institute of Eye Diseases
Abstract
Publisher
Research Institute of Eye Diseases
Reference100 articles.
1. Wiggs JL. Glaucoma Genes and Mechanisms. Prog Mol Biol Transl Sci 2015; 134:315-342. https://doi.org/10.1016/BS.PMBTS.2015.04.008
2. Burdon KP, Mitchell P, Lee A, et al. Association of open-angle glaucoma loci with incident glaucoma in the Blue Mountains Eye Study. Am J Ophthalmol 2015; 159(1):31-36.e1. https://doi.org/10.1016/J.AJO.2014.09.020
3. Normando EM, Yap TE, Maddison J, et al. A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells). Expert Review of Molecular Diagnostics 2020; 20(7):737-748. https://doi.org/10.1080/14737159.2020.1758067
4. Corazza P, Maddison J, Bonetti P, et al. Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology. Expert Review of Molecular Diagnostics 2021;21(1):109-118. https://doi.org/10.1080/14737159.2020.1865806
5. Ichhpujani P, Thakur S, Sahi RK, Kumar S. Validating tablet perimetry against standard Humphrey Visual Field Analyzer for glaucoma screening in Indian population. Indian Journal of Ophthalmology 2021; 69(1):87. https://doi.org/10.4103/IJO.IJO_1847_19
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