Artificial Intelligence in Cardiac Remodeling Assessment: From Models to Clinical Integration

Artificial intelligence (AI) is increasingly being applied to the assessment of cardiac remodeling across structural, functional, and electrophysiological domains. In structural remodeling, AI supports automated chamber segmentation, volumetric quantification, tissue characterization, and identification of remodeling subtypes from imaging data. In functional assessment, AI improves measurement of ventricular function, strain, and hemodynamic parameters, and supports the detection of subclinical dysfunction and longitudinal monitoring of dynamic changes. In electrophysiological remodeling, AI facilitates the analysis of electrocardiographic signals, electrocardiogram (ECG) images, and optical mapping data to detect concealed conduction abnormalities, estimate arrhythmic risk, and support mechanistic interpretation. Multimodal data fusion further enhances risk assessment by integrating imaging, electrocardiography, and clinical information. This article reviews the current state of AI applications in cardiac remodeling and examines how these technologies enable more comprehensive, personalized evaluations, shifting practice from single-parameter assessment to multidimensional prediction. Furthermore, we discuss the key challenges and limitations that must be addressed to realize this potential, along with feasible solutions and future directions for the field.