Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.

A growing number of studies have reported a link between vascular damage and glaucoma based on optical coherence tomography angiography (OCTA) imaging. This multitude of studies focused on different regions of interest (ROIs) which offers the possibility to draw conclusions on the most discriminative locations to diagnose glaucoma. The objective of this work was to review and analyse the discriminative capacity of vascular density, retrieved from different ROIs, on differentiating healthy subjects from glaucoma patients. PubMed was used to perform a systematic review on the analysis of glaucomatous vascular damage using OCTA. All studies up to 21 April 2019 were considered. The ROIs were analysed by region (macula, optic disc and peripapillary region), layer (superficial and deep capillary plexus, avascular, whole retina, choriocapillaris and choroid) and sector (according to the Garway–Heath map). The area under receiver operator characteristic curve (AUROC) and the statistical difference (p‐value) were used to report the importance of each ROI for diagnosing glaucoma. From 96 screened studies, 43 were eligible for this review. Overall, the peripapillary region showed to be the most discriminative region with the highest mean AUROC (0.80 ± 0.09). An improvement of the AUROC from this region is observed when a sectorial analysis is performed, with the highest AUROCs obtained at the inferior and superior sectors of the superficial capillary plexus in the peripapillary region (0.86 ± 0.03 and 0.87 ± 0.10, respectively). The presented work shows that glaucomatous vascular damage can be assessed using OCTA, and its added value as a complementary feature for glaucoma diagnosis depends on the region of interest. A sectorial analysis of the superficial layer at the peripapillary region is preferable for assessing glaucomatous vascular damage.

Optical coherence tomography (OCT) is an optical imaging modality based on low-coherence interferometry. It is a noninvasive technique that provides in vivo cross-sectional images of microscopic structures with high spatial and temporal resolutions, making it an appealing technique for multiple areas in preclinical and clinical research.¹

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.

The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 29 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analysed. The results show a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches, highlighting the importance and need of standardized methodologies for ONH segmentation. Only with a concrete set of guidelines, these automatic segmentation algorithms will build trust in data-driven segmentation models and be able to enter clinical practice.