Cardiovascular imaging—including echocardiography, cardiac MRI, CT angiography, nuclear imaging, and multimodal physiological examinations—plays a central role in the diagnosis, risk stratification, and treatment planning of cardiovascular diseases. With the rapid development of artificial intelligence (AI), particularly deep learning, multimodal fusion, and generative modeling, groundbreaking opportunities are emerging for enhanced image reconstruction, automated quantification, disease prediction, and large-scale population-level cardiac analysis.

This call for papers project aims to bring together cutting-edge research that explores the integration of AI into cardiovascular imaging and diagnostic workflows. We welcome studies that advance methodological innovation, clinical translation, interpretability, multimodal fusion, and intelligent decision support for cardiac disease evaluation.

Topics of Interest include:

1. AI-based analysis in echocardiography, cardiac MRI, CT, PET-CT, and multimodal cardiac imaging
2. Automatic segmentation and quantification of cardiac structures (e.g., ventricles, myocardium, valves, coronary arteries)
3. Deep learning for cardiac function evaluation (EF, strain, wall motion, perfusion)
4. AI-driven detection and prediction of coronary artery disease, cardiomyopathy, arrhythmias, and heart failure
5. AI Vision–language models, large multimodal models (LMMs), and generative AI for cardiac imaging

Submission Details:

Submission Deadline: 31 August 2026
Authors Instruction: https://www.sciltp.com/journals/aim/instructionForAuthors
Please contact the editorial office at aim@sciltp.com if you have any questions.