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Related Experiment Video

Updated: Jul 8, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

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

Ke Li1, Yirui Jiang1, Huan Sun2

  • 1Cardiology Department, China-Japan Union Hospital of Jilin University.

Journal of Visualized Experiments : Jove
|July 6, 2026
PubMed
Summary

Artificial intelligence (AI) enhances cardiac remodeling assessment by analyzing structural, functional, and electrophysiological data. This technology enables personalized, multidimensional predictions for improved patient care.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiac remodeling is a complex process affecting heart structure and function.
  • Current assessment methods often rely on single parameters, limiting comprehensive evaluation.
  • Artificial intelligence offers novel approaches to analyze diverse cardiac data.

Purpose of the Study:

  • To review current artificial intelligence (AI) applications in cardiac remodeling assessment.
  • To explore how AI enables comprehensive, personalized, and multidimensional cardiac evaluations.
  • To discuss challenges, limitations, and future directions for AI in this field.

Main Methods:

  • AI applications in structural remodeling: automated segmentation, quantification, and characterization from imaging data.

Related Experiment Videos

Last Updated: Jul 8, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

  • AI applications in functional assessment: improved measurement of ventricular function, strain, hemodynamics, and subclinical dysfunction detection.
  • AI applications in electrophysiological remodeling: analysis of ECG and optical mapping data for abnormality detection and risk stratification.

    • Main Results:

    • AI facilitates automated and detailed analysis across structural, functional, and electrophysiological domains of cardiac remodeling.
    • Multimodal data fusion with AI enhances risk assessment by integrating imaging, ECG, and clinical data.
    • AI shifts practice towards multidimensional prediction, moving beyond single-parameter assessments.

    Conclusions:

    • AI significantly advances the comprehensive and personalized assessment of cardiac remodeling.
    • Addressing current challenges is crucial for realizing the full potential of AI in cardiology.
    • Future directions involve further integration and refinement of AI tools for predictive cardiac care.