Automated grading of diabetic retinopathy and Radiomics analysis on ultra-wide optical coherence tomography angiography scans

Abstract

Diabetic retinopathy (DR), a progressive condition due to diabetes that can lead to blindness, is typically characterized by a number of stages, including non-proliferative (mild, moderate and severe) and proliferative DR. These stages are marked by various vascular abnormalities, such as intraretinal microvascular abnormalities (IRMA), neovascularization (NV), and non-perfusion areas (NPA). Automated detection of these abnormalities and grading the severity of DR are crucial for computer-aided diagnosis. Ultra-wide optical coherence tomography angiography (UW-OCTA) images, a type of retinal imaging, are particularly well-suited for analyzing vascular abnormalities due to their prominence on these images. However, accurate detection of abnormalities and subsequent grading of DR is quite challenging due to noisy data, presence of artifacts, poor contrast and subtle nature of abnormalities. In this work, we aim to develop an automated method for accurate grading of DR severity on UW-OCTA images. Our method consists of various components such as UW-OCTA scan quality assessment, segmentation of vascular abnormalities and grading the scans for DR severity. Applied on publicly available data from Diabetic retinopathy analysis challenge (DRAC 2022), our method shows promising results with a Dice overlap metric and recall values of 0.88 for abnormality segmentation, and the coefficient-of-agreement ($\kappa$) value of 0.873 for DR grading. We also performed a radiomics analysis, and observed that the radiomics features are significantly different for increasing levels of DR severity. This suggests that radiomics could be used for multimodal grading and further analysis of DR, indicating its potential scope in this area.