Foveal Avascular Zone Volume

Abstract

Purpose: To propose a new clinical evaluation index, foveal avascular zone (FAZ) volume, and analyze its statistical significance. Methods: A semiautomatic method is proposed to measure the FAZ volume in optical coherence tomography angiography images as follows: The region of interest was flattened and annotated axially. The labeled pixels in the restored region of interest were counted as the FAZ volume. Linear regression and the independent samples t-test were performed for the statistical analysis. Results: Sixty-one normal, 64 high myopia, and 42 diabetic retinopathy eyes were imaged using optical coherence tomography angiography. For normal eyes, the FAZ volume correlates inversely with central macular thickness (superficial: P = 0.004; deep: P < 0.001) and positively with area (P < 0.001). For high myopia eyes, the deep plexus FAZ (P = 0.34) and total FAZ (P = 0.38) volumes show no significant difference, whereas the superficial plexus FAZ volume is significantly larger than control (P < 0.001). For diabetic retinopathy eyes, the superficial plexus FAZ (P = 0.001), deep plexus FAZ (P = 0.014), and total volumes (P = 0.002) are significantly larger than control. Conclusion: The FAZ volume is proposed for depicting the 3D structure of the FAZ. It shows greater sensitivity for vascular alteration that makes it meaningful for clinical analysis.

Artificial intelligence and hemodynamic studies in optical coherence tomography angiography for diabetic retinopathy evaluation: A review

“…Four layers of the suggested ANN structure for DR classification were proposed by Abdelsalam, seven neurons form the input layer of the ANN network, representing the seven extracted features with two hidden layers. One output layer with three neurons for the three classification stages of DR. 75,76 The suggested methodology achieved the highest performance indicators of 97% accuracy. The performance indicators in terms of accuracy, sensitivity and specificity of various ML classification models are plotted in bar chart.…”

“…73,74 Certain investigations of DL and ML frameworks in DR screening using OCTA Various ML and DL techniques were implemented and available to diagnose and classify DR automatically using fundus image but few limitations are there in those modeled algorithms as a fundus image is a 2D image where deep blood vessel features are missing, it produces an image with poor resolution so, hemodynamic analysis using ML technique is not possible. [75][76][77] Various DL frameworks and ML algorithms using OCT angiography images for screening and classifying DR were considered for this review.…”