scholarly journals Quantitative Retinal Optical Coherence Tomography Angiography in Patients With Diabetes Without Diabetic Retinopathy

2017 ◽  
Vol 58 (3) ◽  
pp. 1766 ◽  
Author(s):  
Fangyao Tang ◽  
Carol Y. Cheung
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence ◽  
Patients With Diabetes

scholarly journals Analysis of Foveal Microvascular Abnormalities in Diabetic Retinopathy Using Optical Coherence Tomography Angiography with Projection Artifact Removal

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lun Liu ◽  
Jian Gao ◽  
Weili Bao ◽  
Chengyang Hu ◽  
Yajing Xu ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Vessel Density ◽  
Foveal Avascular Zone ◽  
Optical Coherence ◽  
Artifact Removal ◽  
Significant Difference ◽  
Patients With Diabetes ◽  
Capillary Plexus

Purpose. To analyze foveal microvascular abnormalities in different stages of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) with projection artifact removal (PAR). Methods. We analyzed 93 eyes of 59 patients with diabetes—31 with no DR (no DR), 34 with mild to moderate nonproliferative DR (mild DR), and 28 with severe nonproliferative DR to proliferative DR (severe DR)—and 31 age-matched healthy controls. Sections measuring 3 × 3 mm2 centered on the fovea were obtained using OCTA. The area, perimeter, and acircularity index (AI) of the foveal avascular zone (FAZ), vessel density within a 300 μm wide region of the FAZ (FD-300), and parafoveal vessel density in the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were calculated using novel built-in software with PAR. Results. There was no statistically significant difference in the FAZ area (p=0.162). There was a statistically significant difference in the FAZ perimeter (p=0.010) and the AI (p<0.001) between the four groups. There was a correlation between the AI and the increasing severity of DR (p=0.010). Statistically significant decreases of vessel density in the FD-300, SCP, and DCP were observed (all p<0.001). There was a difference in parafoveal vessel density in the DCP between the healthy control eyes and the eyes with diabetes without DR (p=0.027). There was a significant correlation between vessel density and increasing severity of DR (p<0.001). Conclusion. Compared with the FAZ area, AI allows a more helpful quantitative assessment of the changes in the FAZ. Vessel density determined using OCTA with PAR might be a useful parameter indicating the progression of DR. Parafoveal vessel density in the DCP after PAR might be a potential early biomarker of DR before appearance of clinically evident retinopathy and needs further investigation.

scholarly journals Optical Coherence Tomography Angiography in Diabetes and Diabetic Retinopathy

2020 ◽  
Vol 9 (6) ◽  
pp. 1723 ◽  
Author(s):  
Jacqueline Chua ◽  
Ralene Sim ◽  
Bingyao Tan ◽  
Damon Wong ◽  
Xinwen Yao ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Vision Loss ◽  
Longitudinal Design ◽  
Optical Coherence ◽  
Retinal Microvasculature ◽  
Retinal Capillary ◽  
Patients With Diabetes ◽  
Motion Contrast

Diabetic retinopathy (DR) is a common complication of diabetes mellitus that disrupts the retinal microvasculature and is a leading cause of vision loss globally. Recently, optical coherence tomography angiography (OCTA) has been developed to image the retinal microvasculature, by generating 3-dimensional images based on the motion contrast of circulating blood cells. OCTA offers numerous benefits over traditional fluorescein angiography in visualizing the retinal vasculature in that it is non-invasive and safer; while its depth-resolved ability makes it possible to visualize the finer capillaries of the retinal capillary plexuses and choriocapillaris. High-quality OCTA images have also enabled the visualization of features associated with DR, including microaneurysms and neovascularization and the quantification of alterations in retinal capillary and choriocapillaris, thereby suggesting a promising role for OCTA as an objective technology for accurate DR classification. Of interest is the potential of OCTA to examine the effect of DR on individual retinal layers, and to detect DR even before it is clinically detectable on fundus examination. We will focus the review on the clinical applicability of OCTA derived quantitative metrics that appear to be clinically relevant to the diagnosis, classification, and management of patients with diabetes or DR. Future studies with longitudinal design of multiethnic multicenter populations, as well as the inclusion of pertinent systemic information that may affect vascular changes, will improve our understanding on the benefit of OCTA biomarkers in the detection and progression of DR.

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

Detecting papillary neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography

2016 ◽  
Vol 96 (3) ◽  
pp. 321-323 ◽  
Author(s):  
Maria Cristina Savastano ◽  
Matteo Federici ◽  
Benedetto Falsini ◽  
Aldo Caporossi ◽  
Angelo Maria Minnella
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence
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