Cardiovascular risk stratification was recently presented as an accessible outcome for AI algorithms and retinal vascular imaging. Initially, researchers focused on high-incidence ocular diseases such as diabetic retinopathy, age-related macular degeneration or glaucoma. In the field of retinal vascular imaging, AI techniques have found a particularly good fit. However, recent rapid developments in artificial intelligence (AI) in medical imaging hold promise for improved screening, diagnostics and community healthcare. ![]() The field of ocular biomarkers of systemic disease is now conceptualized as “oculomics” research. Hence, impaired retinal vascular network patterns were associated with increased cardiovascular risk score assessment, cardiovascular mortality and cardiovascular risk factors. Numerous studies have investigated the relationship between retinal microvascular abnormalities and systemic system. The retinal vasculature, which can be accessed noninvasively, represents a unique biological model for the study of microvascular abnormalities and pathology associated with cardiovascular disease. However, the microcirculation has been difficult to access, and thus robust microvascular biomarkers have yet to be developed. Indeed, it is recognized that small-vessel or microvascular pathology plays a major role in processes leading to the development of cardiovascular disease (CVD) and its risk factors. Fundus and OCT-A features were associated not only with vascular ocular disorders, but also with systemic vascular diseases. These fundus photograph and OCT-A retinal vascular metrics were shown to be correlated with the detection, severity and progression of various clinical eye diseases (e.g., diabetic retinopathy, vascular occlusion, glaucoma). Effective quantitative retinal vascular metrics, such as vessel density, vessel perfusion and flow index, are available with OCT-A devices. With OCT-A, it is possible to describe the retinal vascular network at capillary level in different plexuses. For example, data on vessel calibre, tortuosity, branching angle and retinal fractal dimension can be extracted from fundus photographs using semi-automated analysis software with reproducible algorithms. Technological developments in modalities such as retinal fundus photography, optical coherence tomography–angiography (OCT-A) or adaptive optics have made it possible to derive accurate retinal vascular metrics. Over the past 2 decades, interest in retinal vascular imaging has been increasing. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research. ![]() A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A new approach called “oculomics” using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. The healthcare burden of cardiovascular diseases remains a major issue worldwide.
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