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      Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network

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          Abstract

          To develop an artificial intelligence (AI) model that predicts anti-vascular endothelial growth factor (VEGF) agent-specific anatomical treatment outcomes in neovascular age-related macular degeneration (AMD), thereby assisting clinicians in selecting the most suitable anti-VEGF agent for each patient. This retrospective study included patients diagnosed with neovascular AMD who received three loading injections of either ranibizumab or aflibercept. Training was performed using optical coherence tomography (OCT) images with an attention generative adversarial network (GAN) model. To test the performance of the AI model, the sensitivity and specificity to predict the presence of retinal fluid after treatment were calculated for the AI model, an experienced (Examiner 1), and a less experienced (Examiner 2) human examiners. A total of 1684 OCT images from 842 patients (419 treated with ranibizumab and 423 treated with aflibercept) were used as the training set. Testing was performed using images from 98 patients. In patients treated with ranibizumab, the sensitivity and specificity, respectively, were 0.615 and 0.667 for the AI model, 0.385 and 0.861 for Examiner 1, and 0.231 and 0.806 for Examiner 2. In patients treated with aflibercept, the sensitivity and specificity, respectively, were 0.857 and 0.881 for the AI model, 0.429 and 0.976 for Examiner 1, and 0.429 and 0.857 for Examiner 2. In 18.5% of cases, the fluid status of synthetic posttreatment images differed between ranibizumab and aflibercept. The AI model using GAN might predict anti-VEGF agent-specific short-term treatment outcomes with relatively higher sensitivity than human examiners. Additionally, there was a difference in the efficacy in fluid resolution between the anti-VEGF agents. These results suggest the potential of AI in personalized medicine for patients with neovascular AMD.

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          Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

          Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
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            Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis.

            Numerous population-based studies of age-related macular degeneration have been reported around the world, with the results of some studies suggesting racial or ethnic differences in disease prevalence. Integrating these resources to provide summarised data to establish worldwide prevalence and to project the number of people with age-related macular degeneration from 2020 to 2040 would be a useful guide for global strategies. We did a systematic literature review to identify all population-based studies of age-related macular degeneration published before May, 2013. Only studies using retinal photographs and standardised grading classifications (the Wisconsin age-related maculopathy grading system, the international classification for age-related macular degeneration, or the Rotterdam staging system) were included. Hierarchical Bayesian approaches were used to estimate the pooled prevalence, the 95% credible intervals (CrI), and to examine the difference in prevalence by ethnicity (European, African, Hispanic, Asian) and region (Africa, Asia, Europe, Latin America and the Caribbean, North America, and Oceania). UN World Population Prospects were used to project the number of people affected in 2014 and 2040. Bayes factor was calculated as a measure of statistical evidence, with a score above three indicating substantial evidence. Analysis of 129,664 individuals (aged 30-97 years), with 12,727 cases from 39 studies, showed the pooled prevalence (mapped to an age range of 45-85 years) of early, late, and any age-related macular degeneration to be 8.01% (95% CrI 3.98-15.49), 0.37% (0.18-0.77), and 8.69% (4.26-17.40), respectively. We found a higher prevalence of early and any age-related macular degeneration in Europeans than in Asians (early: 11.2% vs 6.8%, Bayes factor 3.9; any: 12.3% vs 7.4%, Bayes factor 4.3), and early, late, and any age-related macular degeneration to be more prevalent in Europeans than in Africans (early: 11.2% vs 7.1%, Bayes factor 12.2; late: 0.5% vs 0.3%, 3.7; any: 12.3% vs 7.5%, 31.3). There was no difference in prevalence between Asians and Africans (all Bayes factors <1). Europeans had a higher prevalence of geographic atrophy subtype (1.11%, 95% CrI 0.53-2.08) than Africans (0.14%, 0.04-0.45), Asians (0.21%, 0.04-0.87), and Hispanics (0.16%, 0.05-0.46). Between geographical regions, cases of early and any age-related macular degeneration were less prevalent in Asia than in Europe and North America (early: 6.3% vs 14.3% and 12.8% [Bayes factor 2.3 and 7.6]; any: 6.9% vs 18.3% and 14.3% [3.0 and 3.8]). No significant gender effect was noted in prevalence (Bayes factor <1.0). The projected number of people with age-related macular degeneration in 2020 is 196 million (95% CrI 140-261), increasing to 288 million in 2040 (205-399). These estimates indicate the substantial global burden of age-related macular degeneration. Summarised data provide information for understanding the effect of the condition and provide data towards designing eye-care strategies and health services around the world. National Medical Research Council, Singapore. Copyright © 2014 Wong et al. Open Access article distributed under the terms of CC BY-NC-ND. Published by .. All rights reserved.
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              Ranibizumab for neovascular age-related macular degeneration.

              Ranibizumab--a recombinant, humanized, monoclonal antibody Fab that neutralizes all active forms of vascular endothelial growth factor A--has been evaluated for the treatment of neovascular age-related macular degeneration. In this multicenter, 2-year, double-blind, sham-controlled study, we randomly assigned patients with age-related macular degeneration with either minimally classic or occult (with no classic lesions) choroidal neovascularization to receive 24 monthly intravitreal injections of ranibizumab (either 0.3 mg or 0.5 mg) or sham injections. The primary end point was the proportion of patients losing fewer than 15 letters from baseline visual acuity at 12 months. We enrolled 716 patients in the study. At 12 months, 94.5% of the group given 0.3 mg of ranibizumab and 94.6% of those given 0.5 mg lost fewer than 15 letters, as compared with 62.2% of patients receiving sham injections (P<0.001 for both comparisons). Visual acuity improved by 15 or more letters in 24.8% of the 0.3-mg group and 33.8% of the 0.5-mg group, as compared with 5.0% of the sham-injection group (P<0.001 for both doses). Mean increases in visual acuity were 6.5 letters in the 0.3-mg group and 7.2 letters in the 0.5-mg group, as compared with a decrease of 10.4 letters in the sham-injection group (P<0.001 for both comparisons). The benefit in visual acuity was maintained at 24 months. During 24 months, presumed endophthalmitis was identified in five patients (1.0%) and serious uveitis in six patients (1.3%) given ranibizumab. Intravitreal administration of ranibizumab for 2 years prevented vision loss and improved mean visual acuity, with low rates of serious adverse events, in patients with minimally classic or occult (with no classic lesions) choroidal neovascularization secondary to age-related macular degeneration. (ClinicalTrials.gov number, NCT00056836 [ClinicalTrials.gov].). Copyright 2006 Massachusetts Medical Society.
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                Author and article information

                Contributors
                gentlewt@gmail.com
                kimoph@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 April 2023
                6 April 2023
                2023
                : 13
                : 5639
                Affiliations
                [1 ]GRID grid.61221.36, ISNI 0000 0001 1033 9831, School of Electrical Engineering and Computer Science, , Gwangju Institute of Science and Technology, ; Gwangju, South Korea
                [2 ]INGRADIENT Inc., Seoul, South Korea
                [3 ]GRID grid.61221.36, ISNI 0000 0001 1033 9831, AI Graduated School, , Gwangju Institute of Science and Technology, ; Gwangju, South Korea
                [4 ]MODULABS, Seoul, South Korea
                [5 ]GRID grid.490241.a, ISNI 0000 0004 0504 511X, Department of Ophthalmology, , Kim’s Eye Hospital, ; #156 Youngdeungpo-dong 4ga, Youngdeungpo-gu, Seoul, 150-034 South Korea
                [6 ]GRID grid.490241.a, ISNI 0000 0004 0504 511X, Kim’s Eye Hospital Data Center, ; Seoul, South Korea
                Article
                32398
                10.1038/s41598-023-32398-7
                10079864
                37024576
                db02a6db-231d-4228-a642-8eec7ac24519
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 December 2022
                : 27 March 2023
                Categories
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                © The Author(s) 2023

                Uncategorized
                macular degeneration,outcomes research,machine learning
                Uncategorized
                macular degeneration, outcomes research, machine learning

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