2020
DOI: 10.1016/j.ophtha.2020.03.010
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Application of Automated Quantification of Fluid Volumes to Anti–VEGF Therapy of Neovascular Age-Related Macular Degeneration

Abstract: Purpose: Antievascular endothelial growth factor (VEGF) treatment of neovascular age-related macular degeneration (AMD) is a highly effective advance in the retinal armentarium. OCT offering 3-dimensional imaging of the retina is widely used to guide treatment. Although poor outcomes reported from clinical practice are multifactorial, availability of reliable, reproducible, and quantitative evaluation tools to accurately measure the fluid response, that is, a "VEGF meter," may be a better means of monitoring a… Show more

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Cited by 114 publications
(100 citation statements)
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References 45 publications
(50 reference statements)
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“…Besides the added value of automatically estimated parameters in clinical routine, automatic segmentation of retinal morphology could constitute a pivotal role in the study of underlying disease mechanisms and the identification of targets for new therapeutic strategies. 9,21 A limitation of this study is that the model has been validated only on neovascular AMD. However, training data included other diseases in which anti-VEGF therapy is used, such as diabetic macular edema or retinal vein occlusion.…”
Section: Discussionmentioning
confidence: 99%
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“…Besides the added value of automatically estimated parameters in clinical routine, automatic segmentation of retinal morphology could constitute a pivotal role in the study of underlying disease mechanisms and the identification of targets for new therapeutic strategies. 9,21 A limitation of this study is that the model has been validated only on neovascular AMD. However, training data included other diseases in which anti-VEGF therapy is used, such as diabetic macular edema or retinal vein occlusion.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, automated volumetric quantification of these parameters enables comprehensive study of structure/function correlation and the development of prediction models for treatment response and personalized treatment intervals. 20,21 Several algorithms for the segmentation or quantification of retinal pathology in OCT have been proposed. A limitation of these algorithms is that they often only focus on a single feature, or a subset of relevant features, with segmentation of fluid receiving most attention.…”
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confidence: 99%
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“…This method is so sensitive that even small amount of fluid is detected as remaining SRF. Volumetric analysis of SRF in AMD using artificial intelligence was able to detect fluid at nanoliter level, which was impossible by analysis of cross sectional OCT image [11]. This sensitivity of pixelwise-volume-based measurement might explain that the complete resolution of SRF was noted only less than half of patients while SRF decreased in more than 80% of patients.…”
Section: Discussionmentioning
confidence: 99%
“…Although we relied on this simplified qualitative approach to reflect real-world everyday clinical practice, we acknowledge that it is imperfect and prone to error and misinterpretation [43]. Recent studies have used sophisticated quantification methods of these OCT parameters including customized software and artificial intelligence-based algorithms [43][44][45][46]. An analysis of the FLUID study that quantified IRF and SRF using a deep learning algorithm found that IRF in the central 1 mm of the macula and SRF in the 1 to 6-mm ring were associated with BCVA reduction, however, SRF in the central mm and IRF in the 1 to 6-mm ring were not associated with such reduction.…”
Section: Discussionmentioning
confidence: 99%