Parafoveal Retinal Vessel Density Assessment by Optical Coherence Tomography Angiography in Healthy Eyes

Arya, Malvika; Rebhun, Carl B; Alibhai, A. Yasin; Chen, Xuejing; Neto, Carlos A. Moreira; Baumal, Caroline R.; Reichel, Elias; Witkin, André J.; Duker, Jay S.; Sadda, Srinivas R; Waheed, Nadia K.

doi:10.3928/23258160-20180814-02

Cited by 24 publications

(23 citation statements)

References 29 publications

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“…A negative association between vessel density and increasing DR severity at both the superficial and deep capillary plexuses has been repeatedly identified [60][61][62]. Though quantitative vessel metrics on OCTA have been shown to have low reproducibility between devices, certain OCTA platforms provide color coded perfusion maps based on the vessel density of normal eyes [52]. These maps can help providers track DR severity and assess vessel loss in conjunction to disease progression (Fig.…”

Section: Vascular Changesmentioning

confidence: 95%

“…Though FAZ can be assessed at both the deep capillary plexus and superficial capillary plexus, there is evidence to suggest that the retinal plexuses meet at the edge of the FAZ and it is, therefore, preferable to measure the FAZ on the superficial or the full retinal depth projection [50]. Assessing FAZ area on the full retinal depth projection obviates the need for segmentation and accompanying errors, increasing the measurement's reproducibility [51,52]. Clinically, assessing FAZ enlargement over time can be a useful tool for following DR progression ( Fig.…”

Section: Faz Enlargementmentioning

confidence: 99%

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A practical guide to optical coherence tomography angiography interpretation

2020

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Background Optical coherence tomography angiography (OCTA) can image the retinal vasculature in vivo, without the need for contrast dye. This technology has been commercially available since 2014, however, much of its use has been limited to the research setting. Over time, more clinical practices have adopted OCTA imaging. While countless publications detail OCTA’s use for the study of retinal microvasculature, few studies outline OCTA’s clinical utility. Body This review provides an overview of OCTA imaging and details tips for successful interpretation. The review begins with a summary of OCTA technology and artifacts that arise from image acquisition. New methods and best practices to prevent image artifacts are discussed. OCTA has the unique ability among retinovascular imaging modalities to individually visualize each retinal plexus. Slabs offered in standard OCTA devices are reviewed, and clinical uses for each slab are outlined. Lastly, the use of OCTA for the clinical interpretation of retinal pathology, such as diabetic retinopathy and age-related macular degeneration, is discussed. Conclusion OCTA is evolving from a scientific tool to a clinical imaging device. This review provides a toolkit for successful image interpretation in a clinical setting.

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Section: Vascular Changesmentioning

confidence: 95%

Section: Faz Enlargementmentioning

confidence: 99%

A practical guide to optical coherence tomography angiography interpretation

2020

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“…There have been a number of excellent studies that have highlighted the potential power of OCT-A in discriminating diabetic status. [17][18][19][20][21] In particular, pioneering work by Durbin et al 7 demonstrated the ability of measuring retinal microvasculature to distinguish diabetic status in patients. In their albeit small cohort they demonstrated highest area under the ROC curve (i.e., AUC) of 0.893 for measures of the vessel density in the superficial capillary network.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of Diabetic Status Using Statistical and Machine Learning Techniques on Optical Coherence Tomography Angiography Images

Aslam

Hoyle

Puri

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose To investigate the potential of statistical and machine learning approaches to determine the diabetic status of patients from optical coherence tomography angiography (OCT-A) images. Methods This was a retrospective cross-sectional observational study based at Manchester Royal Eye Hospital, United Kingdom. OCT-A scans were sequentially selected from one eye of each of 182 patients who were either not diabetic, diabetic without retinopathy, or diabetic with retinopathy requiring hospital follow-up. Eligible images were analyzed by expert purpose-built automated algorithms to calculate clinically relevant outcome measures. These were used in turn as inputs to machine learning and statistical procedures to derive algorithms to perform clinically relevant classifications of patient images into the clinical groups. Receiver operating characteristic curves for the classifiers were evaluated and predictive accuracy assessed using area under curve (AUC). Results For distinguishing diabetic patients from those without diabetes, the Random Forest classifier provided the highest AUC (0.8). For distinguishing diabetic patients with significant retinopathy from those with no retinopathy, the highest AUC was represented by logistic regression (0.91). Conclusions The study demonstrates the potential of novel techniques using automated analysis of OCT-A scans to diagnose patients with diabetes, or when diabetic status is known, to automatically determine those that require hospital input. Translational Relevance This work advances the concept of a rapid and noninvasive clinical screening tool using OCT-A to determine a patient's diabetic status.

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“…For contrast, the results of our study were compared with those that instead employed an Otsu global threshold, which is based on minimizing grayscale variance within above-threshold and below-threshold classes and which has also been used in analysis of OCTA metrics, including on CC angiograms. 3 , 24 , 25 The study method was otherwise repeated exactly, and cohort characteristics remained the same. The Figure shows an example of the same angiogram after the binarization step in our study, using the Phansalkar local and Otsu methods.…”

Section: A Casementioning

confidence: 99%