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Impaired retinal blood flow is associated with ocular diseases such as glaucoma, macular degeneration, and diabetic retinopathy. Among several ocular imaging techniques developed to measure retinal blood flow both invasively and non-invasively, adaptive optics (AO)-enabled scanning laser ophthalmoscopy (AO-SLO) resolves individual red blood cells and provides a high resolution with which to measure flow across retinal microvasculature. However, cross-validation of flow measures remains a challenge owing to instrument and patient-specific variability in each imaging technique. Hence, there is a critical need for a well-controlled clinical flow phantom for standardization and to establish blood-flow measures as clinical biomarkers for early diagnosis. Here, we present the design and validation of a simple, compact, portable, linear flow phantom based on a direct current motor and a conveyor-belt system that provides linear velocity tuning within the retinal microvasculature range (0.5–7 mm/s). The model was evaluated using a sensitive AO-SLO line-scan technique, which showed a r2 > 0.997). This model has great potential to calibrate, evaluate, and improve the accuracy of existing clinical imaging systems for retinal blood flow and aid in the diagnosis of ocular diseases with abnormal blood flow.

Over the last two decades, there has been growing interest in assessing corneal biomechanics in different diseases, such as keratoconus, glaucoma, and corneal disorders. Given the interaction and structural continuity between the cornea and sclera, evaluating corneal biomechanics may give us further insights into the pathogenesis, diagnosis, progression, and management of glaucoma. Therefore, some authorities have recommended baseline evaluations of corneal biomechanics in all glaucoma and glaucoma suspects patients. Currently, two devices (Ocular Response Analyzer and Corneal Visualization Schiempflug Technology) are commercially available for evaluating corneal biomechanics; however, each device reports different parameters, and there is a weak to moderate agreement between the reported parameters. Studies are further limited by the inclusion of glaucoma subjects taking topical prostaglandin analogues, which may alter corneal biomechanics and contribute to contradicting results, lack of proper stratification of patients, and misinterpretation of the results based on factors that are confounded by intraocular pressure changes. This review aims to summarize the recent evidence on corneal biomechanics in glaucoma patients and insights for future studies to address the current limitations of the literature studying corneal biomechanics.

Adaptive optics—optical coherence tomography (AO-OCT) is a non-invasive technique for imaging retinal vascular and structural features at cellular-level resolution. Whereas retinal blood vessel density is an important biomarker for ocular diseases, particularly glaucoma, automated blood vessel segmentation tools in AO-OCT have not yet been explored. One reason for this is that AO-OCT allows for variable input axial dimensions, which are not well accommodated by 2D-2D or 3D-3D segmentation tools. We propose a novel bidirectional long short-term memory (LSTM)-based network for 3D-2D segmentation of blood vessels within AO-OCT volumes. This technique incorporates inter-slice connectivity and allows for variable input slice numbers. We compare this proposed model to a standard 2D UNet segmentation network considering only volume projections. Furthermore, we expanded the proposed LSTM-based network with an additional UNet to evaluate how it refines network performance. We trained, validated, and tested these architectures in 177 AO-OCT volumes collected from 18 control and glaucoma subjects. The LSTM-UNet has statistically significant improvement (p < 0.05) in AUC (0.88) and recall (0.80) compared to UNet alone (0.83 and 0.70, respectively). LSTM-based approaches had longer evaluation times than the UNet alone. This study shows that a bidirectional convolutional LSTM module improves standard automated vessel segmentation in AO-OCT volumes, although with higher time cost.