scholarly journals Superficial capillary perfusion on optical coherence tomography angiography differentiates moderate and severe nonproliferative diabetic retinopathy

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0240064 ◽  
Author(s):  
Janice X. Ong ◽  
Changyow C. Kwan ◽  
Maria V. Cicinelli ◽  
Amani A. Fawzi
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence ◽  
Capillary Perfusion ◽  
Nonproliferative Diabetic Retinopathy

Deep Capillary Macular Perfusion Indices Obtained with OCT Angiography Correlate with Degree of Nonproliferative Diabetic Retinopathy

2017 ◽  
Vol 27 (6) ◽  
pp. 716-729 ◽  
Author(s):  
Kumar Sambhav ◽  
Khaled K. Abu-Amero ◽  
Kakarla V. Chalam
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence Tomography Angiography ◽  
Inverse Correlation ◽  
Normal Subjects ◽  
Vessel Density ◽  
Perfusion Index ◽  
Flow Index ◽  
Optical Coherence ◽  
Nonproliferative Diabetic Retinopathy

Purpose To evaluate the integrity of macular and temporomacular vasculature in nonproliferative diabetic retinopathy (NPDR) with noninvasive optical coherence tomography angiography (OCTA) and correlate perfusion indices with degree of NPDR. Methods In this prospective observational cross-sectional study, 102 eyes with newly diagnosed NPDR (mild NPDR, 36; moderate NPDR, 21; severe NPDR, 13; NPDR with diabetic macular edema [DME], 32) underwent OCTA. Sixty eyes of normal subjects served as control. Degree of NPDR (based on Early Treatment Diabetic Retinopathy Study criteria) was confirmed with fluorescein angiography. Automated OCTA/split-spectrum amplitude decorrelation angiography software generated perfusion indices (vessel density and flow index) from images of the retina. The perfusion index of superficial and deep retinal plexuses was obtained in both perifoveal (central 1-3 mm) and parafoveal (3-6 mm) areas. Results Deep plexus parafoveal vessel density was 25.23% (±6.1) in mild NPDR, 20.16% (±6.16) in moderate NPDR, 11.16% (±4.16) in severe NPDR, and 17.91% (±4.42) in NPDR + DME compared to normal subjects (36.93% [±8.1]; (p<0.01). Spearman correlation coefficient (rs) between vessel density and level of NPDR severity in the parafoveal region showed inverse correlation for both superficial (rs -0.87; p = 0.083) and deep (rs -0.96; p = 0.017) plexus. Similarly, when vessel density of the perifoveal region was compared with level of NPDR severity, inverse correlation was noted in both superficial (rs -0.85; p = 0.08) and deep (rs -0.98; p = 0.011) plexus. Conclusions Optical coherence tomography angiography clearly delineated the retinal microcirculation and allowed quantification of vascular perfusion of each layer. As diabetic retinopathy progressed, a decrease in perfusion index is more pronounced in the deep retinal plexus and precedes changes in superficial plexus.

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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