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dc.contributor.authorArslan, J
dc.contributor.authorSamarasinghe, G
dc.contributor.authorBenke, KK
dc.contributor.authorSowmya, A
dc.contributor.authorWu, Z
dc.contributor.authorGuymer, RH
dc.contributor.authorBaird, PN
dc.date.accessioned2020-12-09T22:28:58Z
dc.date.available2020-12-09T22:28:58Z
dc.date.issued2020-01-01
dc.identifierpii: TVST-20-2823
dc.identifier.citationArslan, J., Samarasinghe, G., Benke, K. K., Sowmya, A., Wu, Z., Guymer, R. H. & Baird, P. N. (2020). Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation. TRANSLATIONAL VISION SCIENCE & TECHNOLOGY, 9 (2), https://doi.org/10.1167/tvst.9.2.57.
dc.identifier.issn2164-2591
dc.identifier.urihttp://hdl.handle.net/11343/252978
dc.description.abstractPurpose: The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression). Methods: The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression). Results: Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89. Conclusions: Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds. Translational Relevance: AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.
dc.languageEnglish
dc.publisherASSOC RESEARCH VISION OPHTHALMOLOGY INC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleArtificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation
dc.typeJournal Article
dc.identifier.doi10.1167/tvst.9.2.57
melbourne.affiliation.departmentOphthalmology (Eye & Ear Hospital)
melbourne.source.titleTranslational Vision Science and Technology
melbourne.source.volume9
melbourne.source.issue2
melbourne.source.pages57-
melbourne.identifier.nhmrc1138585
dc.rights.licenseCC BY
melbourne.elementsid1479555
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7594588
melbourne.contributor.authorBaird, Paul
melbourne.contributor.authorGuymer, Robyn
melbourne.contributor.authorBenke, Kurt
melbourne.contributor.authorArslan, Janan
melbourne.contributor.authorWu, Zhichao
dc.identifier.eissn2164-2591
melbourne.identifier.fundernameidNHMRC, 1138585
melbourne.accessrightsOpen Access


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