Guest Editors
Dr. Khalil Khan
Email: k.sirkhan@qu.edu.sa
Affiliation: Department of Information Technology, Qassim University, Buraidah, Saudi Arabia
Homepage:
Research Interests: health analytics, computer vision, machine learning
Summary
Congenital heart disease (CHD) remains one of the most complex and impactful conditions affecting infants, children, and adults worldwide. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful opportunities to enhance the early detection, classification, and clinical management of CHD. From automating image interpretation to predicting surgical risks and long-term outcomes, AI-driven technologies are reshaping both diagnostic pathways and personalized care. However, the integration of these computational tools into real-world CHD practice requires rigorous validation, transparency, and interdisciplinary collaboration. This Special Issue seeks to highlight cutting-edge research that leverages AI/ML to address the pressing challenges of CHD diagnosis, risk assessment, and decision support.
The aim of this Special Issue is to bring together clinicians, engineers, data scientists, and researchers working at the intersection of cardiology and AI to explore innovative approaches for advancing CHD diagnostics. We welcome contributions that develop, evaluate, or implement AI/ML models designed to support clinical workflows, improve diagnostic accuracy, enhance imaging interpretation, and enable individualized patient assessment. Studies involving multimodal datasets—including echocardiography, cardiac MRI/CT, physiological signals, electronic health records, and genetic data—are particularly encouraged. The scope also includes methodological advancements, explainable AI frameworks, and real-world evaluation strategies that promote trustworthy, clinically applicable solutions.
• AI- and ML-based models for early detection and classification of CHD
• Deep learning approaches for echocardiography, MRI, CT, and 3D/4D imaging analysis
• Automated measurement extraction and segmentation for cardiac structures
• Multimodal data fusion integrating imaging, clinical, and genomic features
• Predictive analytics for surgical planning, risk stratification, and outcome forecasting
• Explainable and interpretable AI tools for enhanced clinical understanding
• Real-world validation, fairness, and generalizability of AI systems in CHD care
• Wearable or remote-monitoring AI applications for congenital cardiac conditions
• Benchmark datasets, simulation environments, and reproducibility frameworks
• Ethical, regulatory, and implementation considerations for AI in pediatric and adult CHD
Keywords
congenital heart disease, artificial intelligence, echocardiography, computed tomography, diagnostic modeling, predictive analytics, clinical decision support, precision medicine
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