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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.

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.

In optometry, hyperemia is the accumulation of blood flow in the conjunctival tissue. Dry eye syndrome or allergic conjunctivitis are two of its main causes. Its main symptom is the presence of a red hue in the eye that optometrists evaluate according to a scale in a subjective manner. In this paper, we propose an automatic approach to the problem of hyperemia grading in the bulbar conjunctiva. We compute several image features on images of the patients' eyes, analyse the relations among them by using feature selection techniques and transform the feature vector of each image to the value in the adequate range by means of machine learning techniques. We analyse different areas of the conjunctiva to evaluate their importance for the diagnosis. Our results show that it is possible to mimic the experts' behaviour through the proposed approach.

Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient’s eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctiva