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      Vascular density with optical coherence tomography angiography and systemic biomarkers in low and high cardiovascular risk patients

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          Abstract

          We aimed to compare retinal vascular density in Optical Coherence Tomography Angiography (OCT-A) between patients hospitalized for acute coronary syndrome (ACS) and control patients and to investigate correlation with angiogenesis biomarkers. Patients hospitalized for an acute coronary syndrome (ACS) in the Intensive Care Unit were included in the “high cardiovascular risk” group while patients without cardiovascular risk presenting in the Ophthalmology department were included as “control”. Both groups had blood sampling and OCT-A imaging. Retina microvascularization density in the superficial capillary plexus was measured on 3 × 3 mm angiograms centered on the macula. Angiopoietin-2, TGF-β1, osteoprotegerin, GDF-15 and ST-2 were explored with ELISA or multiplex method. Overall, 62 eyes of ACS patients and 42 eyes of controls were included. ACS patients had significantly lower inner vessel length density than control patients (p = 0.004). A ROC curve found that an inner vessel length density threshold below 20.05 mm −1 was moderately associated with ACS. Significant correlation was found between serum levels of angiopoietin-2 and osteoprotegerin, and retinal microvascularization in OCT-A (R = − 0.293, p = 0.003; R = − 0.310, p = 0.001). Lower inner vessel length density measured with OCT-A was associated with ACS event and was also correlated with higher concentrations of angiopoietin-2 and osteoprotegerin.

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          Most cited references43

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          Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

          Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.
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            Retinal vascular layers imaged by fluorescein angiography and optical coherence tomography angiography.

            The retinal vasculature is involved in many ocular diseases that cause visual loss. Although fluorescein angiography is the criterion standard for evaluating the retina vasculature, it has risks of adverse effects and known defects in imaging all the layers of the retinal vasculature. Optical coherence tomography (OCT) angiography can image vessels based on flow characteristics and may provide improved information.
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              Axial Length Variation Impacts on Superficial Retinal Vessel Density and Foveal Avascular Zone Area Measurements Using Optical Coherence Tomography Angiography.

              To evaluate the impact of image magnification correction on superficial retinal vessel density (SRVD) and foveal avascular zone area (FAZA) measurements using optical coherence tomography angiography (OCTA).
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                Author and article information

                Contributors
                louis.arnould@chu-dijon.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 October 2020
                7 October 2020
                2020
                : 10
                : 16718
                Affiliations
                [1 ]Ophthalmology Department, University Hospital, 14 rue Paul Gaffarel, 21079 Dijon Cedex, France
                [2 ]Laboratoire de Physiopathologie et Epidémiologie Cérébro-Cardiovasculaires (EA7460, PEC2), UFR Des Sciences de Santé, Bourgogne Franche-Comté University, Dijon, France
                [3 ]GRID grid.493090.7, ISNI 0000 0004 4910 6615, Centre des Sciences du Goût et de l’Alimentation, AgroSup Dijon, CNRS, INRAE, , Université Bourgogne Franche-Comté, ; 21000 Dijon, France
                [4 ]GRID grid.7429.8, ISNI 0000000121866389, INSERM, CIC1432, Clinical Epidemiology Unit, ; Dijon, France
                [5 ]GRID grid.31151.37, Dijon University Hospital, Clinical Investigation Center, Clinical Epidemiology/Clinical Trials Unit, ; Dijon, France
                [6 ]GRID grid.31151.37, Cardiology Department, , University Hospital, ; Dijon, France
                Article
                73861
                10.1038/s41598-020-73861-z
                7542456
                33028913
                6a71868d-ebe3-4c49-8222-784c2f04e887
                © The Author(s) 2020

                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
                : 24 April 2020
                : 3 September 2020
                Categories
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                Custom metadata
                © The Author(s) 2020

                Uncategorized
                anatomy,biomarkers
                Uncategorized
                anatomy, biomarkers

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