Correlation between Coronary and Carotid Arteries: A Visual Analysis of Literature Data
Peng Li1, Bai-Ru Cheng1, Yu-Xuan Li1
1Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Insights
The carotid artery serves as a non-invasive indicator for coronary artery disease (CAD) risk. Research trends show a shift towards AI-driven precision diagnosis and personalized patient predictions for cardiovascular health.
Area of Science:
- Cardiovascular Research
- Bibliometrics
- Medical Imaging
Background:
- Early identification of coronary artery disease (CAD) is challenging due to invasive diagnostic methods.
- The superficial carotid artery offers a non-invasive window into overall arterial health and CAD risk.
- Bibliometric analysis is employed to map the research landscape of coronary-carotid artery correlations.
Purpose of the Study:
- To review and visualize research on the correlation between coronary and carotid arteries.
- To illustrate the development and identify hotspots in this research field.
- To evaluate the utility of carotid assessment in predicting coronary artery disease risk.
Main Methods:
- Bibliometric analysis of 419 studies published between 2005 and 2025 from the Web of Science Core Collection.
- Utilized CiteSpace, VOSviewer, and R package for data analysis and visualization.
- Included publications from 62 countries, 890 institutions, and 2,885 authors.
Main Results:
- Research output on coronary-carotid correlation is growing, led by the United States.
- Key terms include atherosclerosis, intima-media thickness, prediction, and coronary artery disease.
- Emerging hotspots focus on atherosclerosis risk assessment, carotid artery as a surrogate marker, and AI/deep learning for diagnosis.
Conclusions:
- The carotid artery is a valuable tool for assessing overall coronary artery disease risk.
- Research has evolved from anatomical correlation to personalized risk prediction.
- AI-assisted ultrasound imaging holds significant potential for enhancing cardiovascular disease diagnosis and treatment.
Background And Objectives:
The early identification of coronary artery disease (CAD) is a challenging task. Direct diagnosis of CAD often requires invasive procedures and complex and expensive imaging techniques. The carotid artery is superficial. It can be easily examined with non-invasive ultrasound. This makes it a potential "window" for overall arterial health and CAD risk. This study uses bibliometric methods. It reviews and visualizes research on the correlation between the coronary and carotid arteries. The goal is to show the field's development, identify research hotspots, and evaluate the use of carotid assessment in CAD risk prediction.
Materials And Methods:
We retrieved and included 419 studies on the coronary-carotid correlation published between January 1, 2005, and September 26, 2025, from the Web of Science Core Collection (WoSCC). Bibliometric analysis and visualization were performed using CiteSpace, VOSviewer, and the R package.
Results:
This study analyzed 419 publications from 62 countries, 890 institutions, and 2,885 authors, published across 204 journals. The United States leads research in this domain. The University of Washington in the U.S. and Queen's University in Canada were the most productive institutions. The most prolific author was SABA L from the University of Cagliari, Italy, with 18 published papers and 516 co-citations. Research output in this area continues to grow. Atherosclerosis published the most articles on this topic, with 39 publications. The core keywords in this field are atherosclerosis, intima-media thickness, prediction, coronary artery disease, and associations. The latest hotspots are atherosclerosis risk assessment, the carotid artery as a coronary 'window' and surrogate marker, and "machine learning" and "deep learning" for precision diagnosis and treatment.
Discussion:
The analysis shows the carotid artery is a useful tool for assessing overall CAD risk. The research focus has clearly changed. It moved from proving anatomical connections to using this knowledge for personal patient predictions. Applying artificial intelligence to the indicator detection of coronary arteries and carotid arteries is expected to enhance the diagnostic level of coronary artery diseases. AI-assisted ultrasound imaging technology may have a profound impact on the diagnosis and treatment of cardiovascular diseases.
Conclusions:
Future work should strengthen broader regional cooperation and forward-looking validation, developing towards more precise and automated approaches to ensure measurable patient benefits.
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