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Artificial Intelligence (AI) is transforming cardiovascular magnetic resonance (CMR) imaging, providing advanced tools for diagnosing and managing cardiovascular diseases. This chapter highlights AI’s role in risk stratification and outcome prediction for conditions like sudden cardiac death (SCD), cardiotoxicity, and heart failure. AI innovations streamline CMR workflows by reducing scan times, managing artifacts, and automating segmentation to improve accuracy in analyzing myocardial tissue and scar quantification. Machine learning (ML) models enhance SCD prediction by integrating imaging data, such as fibrosis patterns from late gadolinium enhancement (LGE), with clinical metrics, offering more personalized risk assessments beyond traditional left ventricular ejection fraction (LVEF) criteria. In oncology, AI-based CMR facilitates early cardiotoxicity detection by identifying subtle myocardial changes through T1/T2 mapping and radiomics. These tools enhance precision, allowing earlier intervention for patients undergoing chemotherapy. In heart failure, AI applications incorporate CMR data and electronic health records to predict hospitalizations and outcomes. Advanced radiomics combined with ML provides personalized risk models, identifying high-risk patients with superior accuracy compared to conventional methods. While AI has revolutionized cardiovascular imaging, challenges persist, including the need for model validation across diverse populations and addressing ethical concerns like data privacy and algorithm transparency. Future directions emphasize integrating multimodal AI systems, including genomic and clinical data, to refine personalized risk prediction and guide treatment strategies. AI’s integration into CMR significantly enhances diagnostic precision, risk stratification, and decision-making, making it a cornerstone for the future of cardiovascular care. This chapter explores its transformative potential and offers insights into addressing existing gaps, ensuring equitable and effective application in clinical practice.

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AI-Driven Risk Stratification in Cardiology: Predicting Cardiovascular Outcomes, Sudden Cardiac Death, Cardiotoxicity, and Heart Failure with CMR

  • 章节

  • 发表日期：

    2025年

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  • Amir Ghaffari Jolfayi,
  • Sara Montazeri,
  • Amirhossein Poopak,
  • Amirali Soheili,
  • Ayda Manzari Tavakoli,
  • Alireza Salmanipour,
  • Sara Adimi,
  • Kiara Rezaei-Kalantari

摘要

Artificial Intelligence (AI) is transforming cardiovascular magnetic resonance (CMR) imaging, providing advanced tools for diagnosing and managing cardiovascular diseases. This chapter highlights AI’s role in risk stratification and outcome prediction for conditions like sudden cardiac death (SCD), cardiotoxicity, and heart failure. AI innovations streamline CMR workflows by reducing scan times, managing artifacts, and automating segmentation to improve accuracy in analyzing myocardial tissue and scar quantification. Machine learning (ML) models enhance SCD prediction by integrating imaging data, such as fibrosis patterns from late gadolinium enhancement (LGE), with clinical metrics, offering more personalized risk assessments beyond traditional left ventricular ejection fraction (LVEF) criteria. In oncology, AI-based CMR facilitates early cardiotoxicity detection by identifying subtle myocardial changes through T1/T2 mapping and radiomics. These tools enhance precision, allowing earlier intervention for patients undergoing chemotherapy. In heart failure, AI applications incorporate CMR data and electronic health records to predict hospitalizations and outcomes. Advanced radiomics combined with ML provides personalized risk models, identifying high-risk patients with superior accuracy compared to conventional methods. While AI has revolutionized cardiovascular imaging, challenges persist, including the need for model validation across diverse populations and addressing ethical concerns like data privacy and algorithm transparency. Future directions emphasize integrating multimodal AI systems, including genomic and clinical data, to refine personalized risk prediction and guide treatment strategies. AI’s integration into CMR significantly enhances diagnostic precision, risk stratification, and decision-making, making it a cornerstone for the future of cardiovascular care. This chapter explores its transformative potential and offers insights into addressing existing gaps, ensuring equitable and effective application in clinical practice.