length to diameter ratio was associated with increased stroke mortality [59]. Chapman et al. reported that ‘optimality’ at arterial branching point in retinal vasculature is affected in men with PVD; the junction exponent, an index of efficiency in haemodynamic, was also deviated from the optimum value. They speculated that this might reflect abnormality of the vascular endothelium because endothelial function plays an important role in maintaining network coordination of branch diameters [93].

8.4Retinal Imaging and Its Potential as a Tool for CVD Risk Prediction

As illustrated in this chapter, the association of retinal signs and CVD has been studied in numerous epidemiological and clinical research settings. Studies have demonstrated prospective associations of retinal signs with future development of CVD. Despite these strong and consistent associations, more translational work is needed to fill the gap between research evidence and clinical practice. For retinal imaging to be clinically useful as a tool for CVD risk prediction, the technique should demonstrate additional prognostic information for conventional CVD risk prediction models such as the Framingham risk score. For example, although retinal vessel calibre had a significant association with increased CHD risk independent of traditional risk factors, the magnitude of information by adding retinal vascular calibre onto the traditional risk prediction model of the Framingham risk score was considered unlikely to change clinical management of patients [88].

Nevertheless, advancements in retinal vascular imaging technologies in the last few decades have provided a non-invasive direct window to quantify subtle morphological features in retinal vasculature. As current grading for retinal images is still highly dependent on human graders, automated assessment to support human grader will contribute to increased cost-effectiveness of retinal screening in CVD risk prediction. To maximize the potential of retinal imaging, advancements in

digital imaging in noise reduction, retinal vessel identification, retinal pathology detection and quantification of vessel geometry or morphology will be critical. Also, the cost-effectiveness of retinal vascular imaging must be evaluated, especially as the measurement of traditional risk factors (e.g. blood pressure, serum cholesterol) is relatively inexpensive and is widely available. Such issues need to be resolved before retinal imaging could be accepted as a clinical tool of CVD risk prediction. To translate these research findings for community benefits, telemedicine or telescreening will be an ideal platform for retinal imaging to achieve this potential.

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