Relationship of Gensini score with retinal vessel diameter and arteriovenous ratio in senile CHD

Correlation of RETINAL Artery Diameter with Coronary Artery Disease: The RETINA CAD Pilot Study-Are the Eyes the Windows to the Heart?

INTRODUCTION

This study aimed to determine whether there was any correlation between coronary artery disease (CAD) and retinal artery diameter at an academic tertiary medical center in Trinidad and Tobago.

METHODS

This prospective study evaluated patients (n = 77) with recent invasive coronary angiography (CAG) and the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score who subsequently underwent optical coherence tomography-angiography (OCT-A) at the Eric Williams Medical Sciences Complex (EWMSC) from January 2021 to March 2021. Routine medical history and cardiovascular medications were also recorded. Spearman's rank correlation coefficient and Mann-Whitney U-tests were used to compare correlations and medians between groups.

RESULTS

The average patient age was 57.8 years old, with the majority being male [n = 55 (71.4%)] and of South Asian ethnicity [n = 53 (68.8%)]. Retinal artery diameter was negatively correlated with the SYNTAX score (-0.332 for the right eye, p = 0.003 and -0.237 for the left eye, p = 0.038). A statistically significant relationship was also demonstrated in females and diabetic patients. There were no serious adverse events (SAEs).

CONCLUSION

A significantly negative correlation was observed between retinal artery diameter and SYNTAX score. This study alludes to the practical use of optical coherence tomography-angiography (OCT-A) as a noninvasive diagnostic modality for patients with cardiovascular disease (CVD). Further large-scale, multicentric studies are required to confirm these exploratory findings.

TRIAL REGISTRATION NUMBER

NCT04233619.

End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net

The retinal vessels in the human body are the only ones that can be observed directly by non-invasive imaging techniques. Retinal vessel morphology and structure are the important objects of concern for physicians in the early diagnosis and treatment of related diseases. The classification of retinal vessels has important guiding significance in the basic stage of diagnostic treatment. This paper proposes a novel method based on generative adversarial networks with improved U-Net, which can achieve synchronous automatic segmentation and classification of blood vessels by an end-to-end network. The proposed method avoids the dependency of the segmentation results in the multiple classification tasks. Moreover, the proposed method builds on an accurate classification of arteries and veins while also classifying arteriovenous crossings. The validity of the proposed method is evaluated on the RITE dataset: the accuracy of image comprehensive classification reaches 96.87%. The sensitivity and specificity of arteriovenous classification reach 91.78% and 97.25%. The results verify the effectiveness of the proposed method and show the competitive classification performance.

Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders

BACKGROUND

Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders.

METHODS

We have conducted a case-control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results.

RESULTS

The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes.

CONCLUSIONS

The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.