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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
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Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.

Raluca Eugenia Iorga1, Damiana Costin2, Răzvana Sorina Munteanu-Dănulescu3

  • 1Department of Surgery II, Discipline of Ophthalmology, "Grigore T. Popa" University of Medicine and Pharmacy, Strada Universitatii No. 16, 700115 Iași, Romania.

Journal of Personalized Medicine
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

Retinal microvascular imaging, using biomarkers like CRAE and AVR, can non-invasively predict cardiovascular disease risk. AI enhances this process, aiding in early detection and prevention.

Keywords:
artificial intelligencecardiovascular diseaseretinal microvascular biomarkersretinal vessel analysis

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Area of Science:

  • Ophthalmology
  • Cardiology
  • Medical Imaging

Background:

  • Cardiovascular disease (CVD) is a leading global cause of mortality.
  • Microcirculatory alterations, particularly in the retinal vasculature, can serve as indicators of cardiovascular risk.
  • Non-invasive analysis of retinal vessels offers a promising avenue for assessing systemic vascular health.

Purpose of the Study:

  • To review current literature on retinal microvascular biomarkers for cardiovascular disease (CVD) prediction.
  • To discuss the methodological advantages of dynamic retinal vessel analysis (DRVA).
  • To identify research gaps and highlight the potential of AI in retinal vascular imaging for CVD screening and monitoring.

Main Methods:

  • Analysis of fundus images to quantify microvascular changes.
  • Measurement of central retinal arteriolar (CRAE) and venular (CRVE) equivalents, and the arteriolar-to-venular diameter ratio (AVR).
  • Utilizing dynamic retinal vessel analysis (DRVA) with flicker light stimulation.
  • Application of Artificial Intelligence (AI) tools like QUARTZ and SIVA-DLS for image analysis.

Main Results:

  • Narrower CRAE, wider CRVE, and lower AVR are associated with increased cardiovascular events.
  • DRVA enables quantification of retinal vascular changes in response to stimuli.
  • AI-driven systems demonstrate efficiency in extracting information from fundus photographs, improving diagnostic accuracy.

Conclusions:

  • Retinal microvascular biomarkers (CRAE, CRVE, AVR) show potential for predicting cardiovascular mortality.
  • AI-powered retinal vascular imaging can aid in cardiovascular risk identification and primary prevention.
  • Further research is needed to explore the clinical application of these biomarkers for systemic vascular health assessment and event prediction.