Matches in SemOpenAlex for { <https://semopenalex.org/work/W4368368123> ?p ?o ?g. }
- W4368368123 abstract "Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups." @default.
- W4368368123 created "2023-05-05" @default.
- W4368368123 creator A5010201196 @default.
- W4368368123 creator A5019003956 @default.
- W4368368123 creator A5040211589 @default.
- W4368368123 creator A5061657921 @default.
- W4368368123 date "2023-05-04" @default.
- W4368368123 modified "2023-10-17" @default.
- W4368368123 title "The application of artificial intelligence in glaucoma diagnosis and prediction" @default.
- W4368368123 cites W1899771023 @default.
- W4368368123 cites W1972054739 @default.
- W4368368123 cites W2012446811 @default.
- W4368368123 cites W2160605010 @default.
- W4368368123 cites W2314681406 @default.
- W4368368123 cites W2322371438 @default.
- W4368368123 cites W2514114492 @default.
- W4368368123 cites W2553640866 @default.
- W4368368123 cites W2557738935 @default.
- W4368368123 cites W2576404523 @default.
- W4368368123 cites W2766593955 @default.
- W4368368123 cites W2792026451 @default.
- W4368368123 cites W2888424632 @default.
- W4368368123 cites W2893356526 @default.
- W4368368123 cites W2895341107 @default.
- W4368368123 cites W2896056014 @default.
- W4368368123 cites W2897507310 @default.
- W4368368123 cites W2899951262 @default.
- W4368368123 cites W2903730647 @default.
- W4368368123 cites W2919115771 @default.
- W4368368123 cites W2943640801 @default.
- W4368368123 cites W2961085424 @default.
- W4368368123 cites W2968303179 @default.
- W4368368123 cites W2969758564 @default.
- W4368368123 cites W2976808722 @default.
- W4368368123 cites W2977922008 @default.
- W4368368123 cites W2981344511 @default.
- W4368368123 cites W2991786700 @default.
- W4368368123 cites W3004396616 @default.
- W4368368123 cites W3008739802 @default.
- W4368368123 cites W3019569624 @default.
- W4368368123 cites W3022840841 @default.
- W4368368123 cites W3022946576 @default.
- W4368368123 cites W3025093592 @default.
- W4368368123 cites W3037488700 @default.
- W4368368123 cites W3040028414 @default.
- W4368368123 cites W3042181089 @default.
- W4368368123 cites W3045215429 @default.
- W4368368123 cites W3045815107 @default.
- W4368368123 cites W3080510794 @default.
- W4368368123 cites W3088596748 @default.
- W4368368123 cites W3092462775 @default.
- W4368368123 cites W3108300834 @default.
- W4368368123 cites W3110220146 @default.
- W4368368123 cites W3114864202 @default.
- W4368368123 cites W3118701753 @default.
- W4368368123 cites W3122325337 @default.
- W4368368123 cites W3136416453 @default.
- W4368368123 cites W3139174434 @default.
- W4368368123 cites W3151530204 @default.
- W4368368123 cites W3153148133 @default.
- W4368368123 cites W3159098702 @default.
- W4368368123 cites W3176580834 @default.
- W4368368123 cites W3179825248 @default.
- W4368368123 cites W3181188811 @default.
- W4368368123 cites W3204139209 @default.
- W4368368123 cites W3206644351 @default.
- W4368368123 cites W4210689231 @default.
- W4368368123 cites W4210879782 @default.
- W4368368123 cites W4213220975 @default.
- W4368368123 cites W4220698442 @default.
- W4368368123 cites W4220847634 @default.
- W4368368123 cites W4220869019 @default.
- W4368368123 cites W4224242326 @default.
- W4368368123 cites W4280620250 @default.
- W4368368123 cites W4281747382 @default.
- W4368368123 cites W4282945766 @default.
- W4368368123 cites W4283797771 @default.
- W4368368123 cites W4293567735 @default.
- W4368368123 cites W4295338533 @default.
- W4368368123 cites W4307905798 @default.
- W4368368123 cites W4308339714 @default.
- W4368368123 cites W4310046115 @default.
- W4368368123 doi "https://doi.org/10.3389/fcell.2023.1173094" @default.
- W4368368123 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37215077" @default.
- W4368368123 hasPublicationYear "2023" @default.
- W4368368123 type Work @default.
- W4368368123 citedByCount "2" @default.
- W4368368123 countsByYear W43683681232023 @default.
- W4368368123 crossrefType "journal-article" @default.
- W4368368123 hasAuthorship W4368368123A5010201196 @default.
- W4368368123 hasAuthorship W4368368123A5019003956 @default.
- W4368368123 hasAuthorship W4368368123A5040211589 @default.
- W4368368123 hasAuthorship W4368368123A5061657921 @default.
- W4368368123 hasBestOaLocation W43683681231 @default.
- W4368368123 hasConcept C108583219 @default.
- W4368368123 hasConcept C118487528 @default.
- W4368368123 hasConcept C119767625 @default.
- W4368368123 hasConcept C119857082 @default.
- W4368368123 hasConcept C154945302 @default.
- W4368368123 hasConcept C2776058522 @default.