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Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A
Suleyman Yaman1, Ozkan Aslan2, Hasan Güler3
1Biomedical Department, Vocational School of Technical Sciences, Firat University, Elazig, Turkey; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
Insights
This review analyzes deep learning models for coronary artery disease (CAD) detection and segmentation. Convolutional Neural Networks (CNNs) dominate, but challenges remain in datasets and model complexity for clinical use.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Coronary artery disease (CAD) is a leading cause of death globally.
- Accurate CAD detection and coronary artery segmentation (CAS) are crucial but labor-intensive.
- Automated methods are needed to improve efficiency and accuracy.
Purpose of the Study:
- To systematically review automated deep learning (DL) models for CAS and CAD detection.
- To cover research published between 2013 and 2024.
- To identify trends, common techniques, and challenges in the field.
Main Methods:
- Systematic literature review following PRISMA methodology.
- Selection of 97 high-impact Q1 studies from 1,589 initial publications.
- Analysis of DL techniques, datasets, imaging modalities, and performance metrics.
Main Results:
- Most studies (97) were published after 2016, focusing on CAS (47) or CAD detection (49).
- CNN-based models are dominant; U-Net is prevalent for CAS using CCTA, while ECG is common for CAD detection.
- Recent trends include attention mechanisms, graph neural networks, and hybrid/multimodal models.
Conclusions:
- Key challenges include limited public datasets, inconsistent performance metrics, and complex models.
- Future research should prioritize larger, diverse datasets and lightweight, explainable AI models.
- Integrating uncertainty quantification is essential for enhancing clinical applicability.
Background:
Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for coronary lesion characterization. Both CAD detection and CAS require expert input, are labor-intensive, and error-prone.
Objectives:
Deep learning (DL) techniques have achieved significant success in CAS and CAD detection, with many studies published recently. This study is an up-to-date systematic review of research on automated DL models for CAS and CAD detection in the past decade (2013-2024).
Method:
Using PRISMA methodology, an initial literature search of 1,589 publications was conducted, from which 97 high-impact Q1 studies were selected based on pre-defined eligibility criteria. These studies were analyzed in terms of DL techniques employed, datasets, modalities, and performance metrics.
Results:
Of the 97 studies, most of which were published after 2016, 47 focused on CAS, 49 on CAD detection, and one on both tasks. CNN-based models were dominant in both domains. For CAS, CCTA was the most frequently used input modality, and U-Net was employed in 38 out of 48 studies, with recent works incorporating attention mechanisms and graph neural networks. ASOCA was the most widely used benchmark dataset. For CAD detection, ECG was the most common modality, with 45 out of 50 studies utilizing CNNs, and 20 of those relying purely on CNN architectures. Hybrid and multimodal models have become more prominent in recent years.
Conclusion:
This review identified several challenges, including limited public datasets, variability in performance metrics, and model complexity. Future studies should focus on larger, diverse datasets and lightweight models integrating explainable artificial intelligence and uncertainty quantification to improve clinical applicability.
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