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Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic
Yimin Qu1, Jack Jock-Wai Lee1, Yuanyuan Zhuo2
1Division of Biostatistics, The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
Insights
Retinal image analysis using the automatic retinal imaging analysis (ARIA) algorithm shows promise for estimating coronary heart disease (CHD) risk in individuals with cardiometabolic disorders. This noninvasive method offers a novel approach to cardiovascular risk assessment.
Area of Science:
- Ophthalmology
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Coronary heart disease (CHD) is a leading global cause of mortality.
- Individuals with cardiometabolic disorders face an increased risk of developing CHD.
- Retinal image analysis offers a noninvasive method to assess microvascular function.
Purpose of the Study:
- To investigate the utility of retinal images for CHD risk estimation in people with cardiometabolic disorders.
- To evaluate the effectiveness of the automatic retinal imaging analysis (ARIA) algorithm for this purpose.
Main Methods:
- A case-control study involving 188 CHD patients and 128 controls with cardiometabolic disorders.
- Retinal images analyzed using the ARIA algorithm to estimate retinal characteristics.
- Machine learning models developed for CHD risk estimation, with sensitivity analysis for diabetes subgroups.
- Validation performed using a ten-fold cross-validation method.
Main Results:
- The CHD risk estimation model achieved 81.3% sensitivity, 88.3% specificity, and 85.4% accuracy.
- The model performed better for CHD patients with diabetes compared to those without diabetes.
Conclusions:
- The ARIA algorithm demonstrates potential as a valuable risk assessment tool for CHD in individuals with cardiometabolic disorders.
- Noninvasive retinal image analysis can aid in identifying individuals at higher risk for cardiovascular events.
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.
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