RETINAL VASCULAR PERFUSION DENSITY MAPPING USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN NORMALS AND DIABETIC RETINOPATHY PATIENTS

Background/aimsSplit-spectrum amplitude decorrelation angiography for spectral-domain optical coherence tomography has enabled detailed, non-invasive assessment of vascular flow. This study evaluates choriocapillaris and retinal capillary perfusion density (CPD) in diabetic eyes using optical coherence tomography angiography (OCTA).MethodsRecords of 136 eyes that underwent OCTA imaging at a single institution were reviewed. Eyes were grouped as non-diabetic controls (37 eyes), patients with diabetes mellitus (DM) without diabetic retinopathy (DM without DR, 31 eyes), non-proliferative diabetic retinopathy (NPDR, 41 eyes) and proliferative diabetic retinopathy (PDR, 27 eyes). Quantitative CPD analyses were performed on OCTA images for assessing perfusion density of the choriocapillaris and retinal plexus for all patients and compared between groups.ResultsEyes with NPDR and PDR showed significantly decreased choriocapillaris CPD compared with controls, while DM eyes without DR did not show significant change. Choriocapillaris whole-image CPD was decreased by 8.3% in eyes with NPDR (p<0.01) and decreased by 7.1% in eyes with PDR (p<0.01). Choriocapillaris parafoveal CPD was decreased by 8.9% in eyes with NPDR (p<0.01) and decreased by 8.2% in eyes with PDR (p<0.01). Compared with controls, only eyes with PDR showed significantly decreased retinal CPD, as well as significantly increased foveal avascular zone (FAZ) area. In those patients, retinal whole-image CPD was decreased by 9.7% (p<0.01), retinal foveal CPD was decreased by 20.5% (p<0.01) and retinal parafoveal CPD was decreased by 11.4% (p<0.01). FAZ area was increased by 50.9% (p<0.01).ConclusionsChoriocapillaris and retinal CPD are reduced in diabetic retinopathy, while FAZ area is increased in eyes with PDR. Vascular changes captured by new imaging modalities can further characterise diabetic choroidopathy.

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Assessment of retinal and optic disc vascular perfusion density in retinitis pigmentosa patients by optical coherence tomography angiography

Ophthalmology Journal ◽

10.5603/oj.2019.0016 ◽

2019 ◽

Vol 4 (0) ◽

pp. 100-104

Author(s):

Katarzyna Baltaziak ◽

Lucyna Baltaziak ◽

Rashed Mustafa Nazzal ◽

Anna Woźniak ◽

Robert Rejdak

Keyword(s):

Optical Coherence Tomography ◽

Retinitis Pigmentosa ◽

Optic Disc ◽

Optical Coherence Tomography Angiography ◽

Optical Coherence ◽

Vascular Perfusion ◽

Perfusion Density

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

Scientific Reports ◽

10.1038/s41598-020-80058-x ◽

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.

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