Imaging Biomarkers in Diabetic Retinopathy and Diabetic Macular Edema

Mehta, Nitish; Tsui, Edmund; Lee, Gregory D.; Modi, Yasha S.

doi:10.1097/iio.0000000000000249

Cited by 9 publications

(8 citation statements)

References 120 publications

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“…The deterioration of these parameters could be presented as a consequence of pericytes reduction in small branching vessels resulting in reduced branching complexity of retinal vasculature. 8 Although the development of DR has not been related to the prediabetic state, patients in this category have been linked to and increased risk of cardiovascular disease (RR = 1.13–1.30), coronary disease (RR = 1.10–1.20), stroke (RR = 1.6–1.20), and mortality rates (RR = 1.13–1.32). Many public health policies and in-office physician efforts are directed toward detecting these higher risk patients on which early measures could alter de natural history of disease.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…New technologies in different retinal diseases have emerged, like optical coherence tomography (OCT) and OCT angiography (OCT-A) being tools of diagnosis, monitoring, and prediction of response to treatment. [6][7][8][9][10][11][12][13][14][15][16][17] It is well-known that the primary ocular injury target of DM is the retinal microvasculature. 11 Several imaging studies to assess retinal vasculature have been developed, specifically, retinal angiography optimized the value of fundoscopy, motivating the interest in vascular analysis tools, such as the retinal vessel analyzer (RVA) 18 and more recently integrated or nonintegrated analytic software of OCT-A 19 and artificial intelligence 20 that aim to increase the detection of more early changes.…”

Section: Introductionmentioning

confidence: 99%

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Early microvascular changes in patients with prediabetes evaluated by optical coherence tomography angiography

et al. 2021

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Background: Timely detection of early microvascular changes in patients with prediabetes could help reduce the likelihood of progression of diabetes-related retinal complications. Aim: To determine early microvascular changes in patients with prediabetes using optical coherence tomography angiography (OCT-A). Methods: In this single-center retrospective case-control study, macular OCT-A images of superficial capillary plexus (SCP) and deep capillary plexus (DCP) were analyzed in non-diabetic controls, and prediabetic and diabetic subjects. A quantitative analysis was performed using ImageJ software of the foveal avascular zone (FAZ) area, acircularity index (AI), perfusion density (PD), and vascular length density (VLD). Results: A total of 94 eyes of 53 patients were included in this study. The global mean age was 57.7 years, 39.6% men and 60.4% women. In SCP, the mean PD was 0.283 ± 0.15, 0.186 ± 0.720, and 0.186 ± 0.07 in non-diabetic controls, and prediabetic and diabetic groups, respectively. The mean VLD was 8.728 ± 3.425 in non-diabetic controls, 6.147 ± 1.399 in prediabetic group, and 6.292 ± 1.997 in patients with diabetes. The comparison of prediabetic patients and controls shows statistical differences between PD and VLD in both plexus SCP ( p = 0.002 and p = 0.001, respectively) and DCP ( p = 0.005 and p = 0.002, respectively). The mean area of FAZ in patients with diabetes and normal individuals was 0.281 and 0.196 mm2, respectively ( p < 0.001). AI was higher in the control group (0.87 ± 0.14) and prediabetic group (0.80 ± 0.17) compared to diabetic patients (0.64 ± 0.19). There were no differences in FAZ area and AI between prediabetic and non-diabetic controls. Conclusion: PD and VLD demonstrated to be early microvascular changes in prediabetic patients evaluated by OCT-A. No alterations of FAZ were evidenced in this group.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Early microvascular changes in patients with prediabetes evaluated by optical coherence tomography angiography

et al. 2021

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“…The dimension of FAZ has been used as retinal markers in many ophthalmic conditions, 1 , 5 and it also helps in prognosis. 6 For example, the FAZ size shows a relationship with the severity of Diabetic Retinopathy (DR), one of the major blinding diseases of the eye. 6 Multiple studies have reported enlargement of the FAZ area in subjects with DR, and it is irregularly contoured with scattered microaneurysms.…”

Section: Introductionmentioning

confidence: 99%

“… 6 For example, the FAZ size shows a relationship with the severity of Diabetic Retinopathy (DR), one of the major blinding diseases of the eye. 6 Multiple studies have reported enlargement of the FAZ area in subjects with DR, and it is irregularly contoured with scattered microaneurysms. 7 High myopia also affects the size and shape of the FAZ.…”

Section: Introductionmentioning

confidence: 99%

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

Viekash¹,

Balaji²,

Lakshminarayanan³

2021

OPTH

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Introduction Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ’s deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). Methods Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. Results The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0–66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. Conclusion A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device’s inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.

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