Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning

Young In Kim ¹, Jae-Hyung Roh ², Jihoon Kweon ³

⁰Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

International Journal of Cardiology +

|

March 13, 2024

View abstract on PubMed

Summary

Artificial intelligence-based quantitative coronary angiography (AI-QCA) offers a promising automated solution for analyzing coronary lesions, reducing variability and improving efficiency in clinical decision-making.

Area Of Science

Cardiology
Medical Imaging
Artificial Intelligence

Background

Quantitative coronary angiography (QCA) provides objective coronary lesion assessment but suffers from observer variability and time constraints.
Current clinical practice faces challenges with on-site QCA due to these limitations.

Purpose Of The Study

To develop and evaluate a novel artificial intelligence-based quantitative coronary angiography (AI-QCA) method for analyzing major coronary vessels.
To assess the performance of AI-QCA in comparison to manual QCA.

Main Methods

AI-QCA was developed using deep learning models trained on 7658 angiographic images for lumen boundary delineation.
An automated quantification method with refined matching and iterative updates was integrated into AI-QCA.
A retrospective analysis compared AI-QCA with manual QCA on 676 coronary angiography images, evaluating diameter stenosis (DS), minimum lumen diameter (MLD), reference lumen diameter (RLD), and lesion length (LL).

Main Results

AI-QCA demonstrated 89% sensitivity in lesion detection with strong correlations to manual QCA for DS, MLD, RLD, and LL.
80% of matched lesions (892/995) showed DS differences of ≤10% compared to manual QCA.
Multiple coronary lesions were accurately identified and quantitatively analyzed by AI-QCA without manual intervention.

Conclusions

AI-QCA shows significant potential as an automated tool for coronary angiography analysis.
This AI-driven approach may enhance quantitative assessment of coronary lesions and support clinical decision-making.

Keywords:

Artificial intelligence Deep-learning Quantitative coronary angiography