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Review Article

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

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DOI:

10.3791/71095

June 22nd, 2026

* These authors contributed equally

In This Article

Summary

This review examines how artificial intelligence transforms the assessment of cardiac remodeling across structural, functional, and electrophysiological domains, and discusses the path towards its clinical integration.

Abstract

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.

Introduction

Cardiac remodeling is an adaptive pathophysiological response that follows initial myocardial injury from conditions such as myocardial infarction. Its hallmarks include progressive changes in chamber geometry, tissue composition, systolic and diastolic function, and electrophysiological properties1. Left unchecked, remodeling may progress to decompensation and lead to heart failure (HF)2. The process encompasses alterations at multiple levels, including gene regulation, molecular signaling, cellular behavior, and the tissue microenvironment. Recent studies indicate that cardiac remodeling and chronic HF retain potential....

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Review and Perspective

Advances in AI for assessing structural cardiac remodeling

AI-based assessment of structural cardiac remodeling has evolved along three main directions: (1) automated chamber segmentation and volumetric quantification, (2) myocardial tissue characterization, and (3) predictive modeling and subtype identification. Across studies, AI algorithms have achieved expert-level accuracy in segmenting cardiac chambers and calculating volumes and ejection fraction from CMR and 3DE, substantially reducing manual workload and inter-operator variability. Beyond volume assessment, AI enables detection and quantification of myocardial fibros....

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Conclusions

Current evidence indicates that AI can outperform conventional human assessment in specific technical tasks, particularly when the goal is high-throughput, reproducible measurement. This advantage is most evident in automated segmentation, volumetric quantification, strain analysis, and ECG-based pattern recognition, where AI may reduce observer variability, shorten analysis time, and detect subtle abnormalities that are difficult to appreciate visually. In these settings, AI is best viewed as a tool that complements and.......

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Disclosures

The project is supported by Jilin Provincial Science and Technology Department (YDZJ202501ZYTS071).

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Tags

Chamber SegmentationVolumetric QuantificationTissue CharacterizationVentricular FunctionElectrophysiological RemodelingElectrocardiographic AnalysisMultimodal Data FusionArrhythmic Risk