Yansha Lu1,2, Joseph M Simonett1, Jie Wang1, Miao Zhang1,3, Thomas Hwang1, Ahmed M Hagag1, David Huang1, Dengwang Li2, Yali Jia1. 1. Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States. 2. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Biomedical Sciences, School of Physics and Electronics, Shandong Normal University, Jinan, China. 3. Optovue, Inc., Fremont, California, United States.
Abstract
Purpose: To describe an automated algorithm to quantify the foveal avascular zone (FAZ), using optical coherence tomography angiography (OCTA), and to compare its performance for diagnosis of diabetic retinopathy (DR) and association with best-corrected visual acuity (BCVA) to that of extrafoveal avascular area (EAA). Methods: We obtained 3 × 3-mm macular OCTA scans in diabetic patients with various levels of DR and healthy controls. An algorithm based on a generalized gradient vector flow (GGVF) snake model detected the FAZ, and metrics assessing FAZ size and irregularity were calculated. We compared the automated FAZ segmentation to manual delineation and tested the within-visit repeatability of FAZ metrics. The correlations of two conventional FAZ metrics, two novel FAZ metrics, and EAA with DR severity and BCVA, as determined by Early Treatment Diabetic Retinopathy Study (ETDRS) charts, were assessed. Results: Sixty-six eyes from 66 diabetic patients and 19 control eyes from 19 healthy participants were included. The agreement between manual and automated FAZ delineation had a Jaccard index > 0.82, and the repeatability of automated FAZ detection was excellent in eyes at all levels of DR severity. FAZ metrics that incorporated both FAZ size and shape irregularity had the strongest correlation with clinical DR grade and BCVA. Of all the tested OCTA metrics, EAA had the greatest sensitivity in differentiating diabetic eyes without clinical evidence of retinopathy, mild to moderate nonproliferative DR (NPDR), and severe NPDR to proliferative DR from healthy controls. Conclusions: The GGVF snake algorithm tested in this study can accurately and reliably detect the FAZ, using OCTA data at all DR severity grades, and may be used to obtain clinically useful information from OCTA data regarding macular ischemia in patients with diabetes. While FAZ metrics can provide clinically useful information regarding macular ischemia, and possibly visual acuity potential, EAA measurements may be a better biomarker for DR.
Purpose: To describe an automated algorithm to quantify the foveal avascular zone (FAZ), using optical coherence tomography angiography (OCTA), and to compare its performance for diagnosis of diabetic retinopathy (DR) and association with best-corrected visual acuity (BCVA) to that of extrafoveal avascular area (EAA). Methods: We obtained 3 × 3-mm macular OCTA scans in diabeticpatients with various levels of DR and healthy controls. An algorithm based on a generalized gradient vector flow (GGVF) snake model detected the FAZ, and metrics assessing FAZ size and irregularity were calculated. We compared the automated FAZ segmentation to manual delineation and tested the within-visit repeatability of FAZ metrics. The correlations of two conventional FAZ metrics, two novel FAZ metrics, and EAA with DR severity and BCVA, as determined by Early Treatment Diabetic Retinopathy Study (ETDRS) charts, were assessed. Results: Sixty-six eyes from 66 diabeticpatients and 19 control eyes from 19 healthy participants were included. The agreement between manual and automated FAZ delineation had a Jaccard index > 0.82, and the repeatability of automated FAZ detection was excellent in eyes at all levels of DR severity. FAZ metrics that incorporated both FAZ size and shape irregularity had the strongest correlation with clinical DR grade and BCVA. Of all the tested OCTA metrics, EAA had the greatest sensitivity in differentiating diabetic eyes without clinical evidence of retinopathy, mild to moderate nonproliferative DR (NPDR), and severe NPDR to proliferative DR from healthy controls. Conclusions: The GGVF snake algorithm tested in this study can accurately and reliably detect the FAZ, using OCTA data at all DR severity grades, and may be used to obtain clinically useful information from OCTA data regarding macular ischemia in patients with diabetes. While FAZ metrics can provide clinically useful information regarding macular ischemia, and possibly visual acuity potential, EAA measurements may be a better biomarker for DR.
Authors: Yang Lu; Miguel O Bernabeu; Jan Lammer; Charles C Cai; Martin L Jones; Claudio A Franco; Lloyd Paul Aiello; Jennifer K Sun Journal: Biomed Opt Express Date: 2016-11-04 Impact factor: 3.732
Authors: Lucie Sawides; Kaitlyn A Sapoznik; Alberto de Castro; Brittany R Walker; Thomas J Gast; Ann E Elsner; Stephen A Burns Journal: Invest Ophthalmol Vis Sci Date: 2017-07-01 Impact factor: 4.799
Authors: Dawn A Sim; Pearse A Keane; Javier Zarranz-Ventura; Simon Fung; Michael B Powner; Elise Platteau; Catey V Bunce; Marcus Fruttiger; Praveen J Patel; Adnan Tufail; Catherine A Egan Journal: Invest Ophthalmol Vis Sci Date: 2013-03-28 Impact factor: 4.799
Authors: Giselle Lynch; Jorge S Andrade Romo; Rachel Linderman; Brian D Krawitz; Shelley Mo; Amir Zakik; Joseph Carroll; Richard B Rosen; Toco Y P Chui Journal: Biomed Opt Express Date: 2018-11-05 Impact factor: 3.732
Authors: Ahmed M Hagag; Jie Wang; Kevin Lu; Gareth Harman; Richard G Weleber; David Huang; Paul Yang; Mark E Pennesi; Yali Jia Journal: Am J Ophthalmol Date: 2019-03-06 Impact factor: 5.258
Authors: Yi-Ting Hsieh; Minhaj Nur Alam; David Le; Chia-Chieh Hsiao; Chang-Hao Yang; Daniel L Chao; Xincheng Yao Journal: Ophthalmol Retina Date: 2019-05-07