Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions

Abstract

The black void behind the pupil was optically impenetrable before the invention of the ophthalmoscope by von Helmholtz over 150 years ago. Advances in retinal imaging and image processing, especially over the past decade, have opened a route to another unexplored landscape, the retinal neurovascular architecture and the retinal ganglion pathways linking to the central nervous system beyond. Exploiting these research opportunities requires multidisciplinary teams to explore the interface sitting at the border between ophthalmology, neurology and computing science. It is from the detail and depth of retinal phenotyping that novel metrics and candidate biomarkers are likely to emerge. Confirmation that in vivo retinal neurovascular measures are predictive of microvascular change in the brain and other organs is likely to be a major area of research activity over the next decade. Unlocking this hidden potential within the retina requires integration of structural and functional data sets, that is, multimodal mapping and longitudinal studies spanning the natural history of the disease process. And with further advances in imaging, it is likely that this area of retinal research will remain active and clinically relevant for many years to come. Accordingly, this review looks at state-of-the-art retinal imaging and its application to diagnosis, characterization and prognosis of chronic illness or long-term conditions.

Figure 1.

Anatomy of the adult human eye and retinal layers. (a) Sagittal view showing the main structures. (b) Diagram of the organization of the retinal layers and cells.

Figure 2.

Imaging the retina. (a) Fundus camera image acquired with a Canon CR-DGi (Canon, Tokyo, Japan) non-mydriatic camera at 45° field of view (FOV) and (b) scanning laser ophthalmoscope image acquired with an Optos® P200C (Optos, Dunfermline, Scotland) at approximately 200° FOV. Although not of the same person, the images demonstrate the difference in area viewable with the two devices, which is revealed by comparing the optic nerve head (i.e. the bright circular region) as it appears in each image.

Figure 3.

Multimodal retinal imaging. (a) Scanning laser ophthalmoscope image focused on OD, showing peripapillary optical coherence tomography (OCT) scan protocol (white circle), and (b) OCT image showing retinal layers, including retinal nerve fibre layer (RNFL) and others below. Acquired with Heidelberg SPECTRALIS® OCT (Heidelburg Engineering, Heidelberg, Germany). Segmentation of RNFL quantifies temporal thinning: a described finding in multiple sclerosis.

Figure 4.

Features of interest in a fundus image. The optic nerve head boundary provides a reference point for locating and quantifying other features of interest. Measurement of parameters such as the vessel diameter, bifurcation geometry and vascular tortuosity (i.e. how much it twists and turns) can reveal a suboptimal microvascular network in the retina, which may be related to microvascular damage and an indicator of disease. Different diseases may affect arterioles and venules differently, so differentiating between the two types of vessels is important.

Figure 5.

Scanning laser ophthalmoscope (SLO) image showing signs of proliferative diabetic retinopathy, with diabetic maculopathy. With a wider field of view than a fundus camera, diabetic retinopathy signs in the periphery as well as near to the macula are identifiable in a single image acquisition. Acquired with Optos® P200C SLO (Optos, Dunfermline, Scotland).

Figure 6.

Fundus camera image showing signs of hypertensive retinopathy, i.e. arteriovenous nicking indicated by arrows. Image acquired with a Canon CR-DGi (Canon, Tokyo, Japan) non-mydriatic camera at 45° field of view.

Figure 7.

Automatic retinal vessel detection. The output is a vessel map showing vessel locations in white. Examples shown are for the fundus images in Figure 2, with (a) the resulting map for the fundus camera and (b) the map for scanning laser ophthalmoscope. Vessel detection is the precursor to the measurement of parameters such as vessel diameter, bifurcation geometry and vascular tortuosity. The complexity of the map can also be quantified using fractal analysis to yield a fractal dimension that combines contributions of individual vessel parameters into a single global value.

Figure 8.

Optical coherence tomography (OCT) volume scan co-registered with fundus image from scanning laser ophthalmoscope. Acquired with Heidelberg SPECTRALIS® OCT (Heidelburg Engineering, Heidelberg, Germany).