AUTHOR=Wang Xiao , Wang Junfeng , Wang Wenjun , Zhu Mingxiang , Guo Hua , Ding Junyu , Sun Jin , Zhu Di , Duan Yongjie , Chen Xu , Zhang Peifang , Wu Zhenzhou , He Kunlun 

TITLE=Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.945451

DOI=10.3389/fcvm.2022.945451

ISSN=2297-055X

ABSTRACT=Background 
Coronary artery disease (CAD) is the progressive development of the heart supply vessel disease, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timeous intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is valuable to shed light on the development of new imaging diagnostic markers.
Objective 
We aim to investigate and summarize how AI algorithms were used in 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 
Forty-six articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomographic (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 (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 a few studies (4/46, 8.7%) carried out external validation of the AI model.
Conclusion 
As non-invasive diagnostic methods, imaging markers developed with AI showed  their ability in diagnosis of CAD. External validation of model performance and evaluation of clinical usefulness would be useful to confirm the added value of the markers in practice.