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Updated: Aug 24, 2025
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging
Xiao Wang1,2,3, Junfeng Wang4, Wenjun Wang1,2,3
1Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China.
Insights
Artificial intelligence (AI) enhances coronary artery disease (CAD) diagnosis using imaging markers. While deep learning models show promise, further external validation is needed for clinical application.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) is a life-threatening condition requiring early diagnosis.
- Imaging tests are crucial for CAD diagnosis, with AI emerging as a tool for developing new diagnostic markers.
- AI integration aims to improve the accuracy and efficiency of CAD detection.
Purpose of the Study:
- To review and summarize the application of AI algorithms in developing diagnostic models for CAD using imaging markers.
- To identify common imaging modalities and AI techniques used in CAD diagnosis.
- To assess the performance and limitations of AI-driven diagnostic models for CAD.
Main Methods:
- A scoping review was conducted following the PRISMA-ScR guidelines.
- Literature search was performed in PubMed and Embase, selecting articles on coronary heart disease.
- Data extraction and narrative synthesis were employed for analysis.
Main Results:
- 46 articles were included, with single-photon emission computed tomography and coronary computed tomography angiography being the most common imaging methods.
- Deep learning algorithms (89.2%) were predominantly used over machine learning.
- AI models demonstrated good performance, but most studies had small sample sizes and limited external validation.
Conclusions:
- AI-integrated imaging markers show significant potential for non-invasive CAD diagnosis.
- External validation and clinical utility assessment are crucial to confirm the practical value of these AI-driven diagnostic tools.
- Further research with larger datasets and external validation is recommended to translate AI models into clinical practice.
Background:
Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers.
Objective:
We aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers.
Methods:
This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis.
Results:
A total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model.
Conclusion:
As non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice.
Systematic Review Registration:
[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].
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