Comparison of widefield swept-source optical coherence tomographic angiography and fluorescein fundus angiography for detection of retinal neovascularization with diabetic retinopathy

OCT angiography 2023 update: focus on diabetic retinopathy

Optical coherence tomography angiography (OCTA) has become part of the clinical practice and its growing applications are in continuous development. Coherently with the growing concern about the human and economic cost of diabetes, diabetic retinopathy (DR) was the most popular topic for OCTA studies in the past year. The analysis of the literature reveals that applications of OCTA in DR are in continuous growth. In particular, ultrawide field (UWF) OCTA and artificial intelligence (AI) based on OCTA images are affirming as the new frontiers of scientific research in the field. Diagnostic accuracy of AI methods based on OCTA is equal or superior to the one based on OCT methods and also bears potential to detect systemic associations. UWF OCTA is noninvasive method that is reaching similar accuracy of FA in detection of neovascularization and intraretinal microvascular abnormalities (IRMAs) and has allowed better characterization of microvascular peripherical changes in DR. Lastly, deep capillary plexus (DCP) characteristics seem to play a pivotal role in the development of diabetic macular edema (DME) and refinement of biomarkers for different phenotypes of DME and diabetic macular ischemia (DMI) is currently on its way.

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed.