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Cardiovascular diseases, particularly cardiomyopathies, remain leading contributors to global morbidity and mortality, necessitating innovative diagnostic and therapeutic solutions. This chapter explores the transformative potential of Artificial Intelligence (AI) in cardiac magnetic resonance imaging (CMR) for precision diagnosis and management of hypertrophic, dilated, restrictive, and arrhythmogenic cardiomyopathies. CMR is the gold standard for evaluating myocardial structure and function but is often constrained by cost, expertise requirements, and accessibility. AI-driven advancements, including deep learning algorithms, promise to overcome these limitations by automating image analysis, enhancing diagnostic accuracy, and enabling cost-effective non-contrast imaging protocols. Hypertrophic cardiomyopathy (HCM) benefits from AI-enhanced CMR modalities, such as T1/T2 mapping and cine imaging, for detecting fibrosis and hypertrophy, crucial for sudden cardiac death risk stratification. Similarly, in dilated cardiomyopathy (DCM), AI enables automated segmentation of ventricular structures, precise fibrosis quantification via late gadolinium enhancement (LGE), and improved risk models incorporating clinical and genetic data. For restrictive cardiomyopathy (RCM), AI leverages advanced mapping techniques to differentiate infiltrative and fibrotic pathologies, overcoming diagnostic ambiguities. Arrhythmogenic cardiomyopathy (ACM), characterized by fibrofatty myocardial replacement, sees significant advancements with AI-optimized ECG interpretation and automated CMR segmentation, addressing challenges in phenotypic variability and risk prediction. Emerging technologies integrated with AI further enhance patient accessibility, reduce costs, and improve scan efficiency. AI’s role in standardizing interpretations and supporting personalized medicine underscores its transformative potential. By integrating AI with advanced imaging protocols, this chapter highlights a pathway to scalable, accurate, and patient-centric solutions for managing cardiomyopathies, paving the way for precision cardiology.

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AI-Powered MRI for Precision Diagnosis of Cardiomyopathy: Insights into Hypertrophic, Dilated, Arrhythmogenic, and Restrictive Cardiomyopathies

  • 章节

  • 发表日期：

    2025年

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  • Amir Ghaffari Jolfayi,
  • Mohammadhossein Mozafarybazargany,
  • Hamed Ghoshouni,
  • Elham Shabani,
  • Maedeh Dastmardi,
  • Alireza Salmanipour,
  • Golnaz Houshmand

摘要

Cardiovascular diseases, particularly cardiomyopathies, remain leading contributors to global morbidity and mortality, necessitating innovative diagnostic and therapeutic solutions. This chapter explores the transformative potential of Artificial Intelligence (AI) in cardiac magnetic resonance imaging (CMR) for precision diagnosis and management of hypertrophic, dilated, restrictive, and arrhythmogenic cardiomyopathies. CMR is the gold standard for evaluating myocardial structure and function but is often constrained by cost, expertise requirements, and accessibility. AI-driven advancements, including deep learning algorithms, promise to overcome these limitations by automating image analysis, enhancing diagnostic accuracy, and enabling cost-effective non-contrast imaging protocols. Hypertrophic cardiomyopathy (HCM) benefits from AI-enhanced CMR modalities, such as T1/T2 mapping and cine imaging, for detecting fibrosis and hypertrophy, crucial for sudden cardiac death risk stratification. Similarly, in dilated cardiomyopathy (DCM), AI enables automated segmentation of ventricular structures, precise fibrosis quantification via late gadolinium enhancement (LGE), and improved risk models incorporating clinical and genetic data. For restrictive cardiomyopathy (RCM), AI leverages advanced mapping techniques to differentiate infiltrative and fibrotic pathologies, overcoming diagnostic ambiguities. Arrhythmogenic cardiomyopathy (ACM), characterized by fibrofatty myocardial replacement, sees significant advancements with AI-optimized ECG interpretation and automated CMR segmentation, addressing challenges in phenotypic variability and risk prediction. Emerging technologies integrated with AI further enhance patient accessibility, reduce costs, and improve scan efficiency. AI’s role in standardizing interpretations and supporting personalized medicine underscores its transformative potential. By integrating AI with advanced imaging protocols, this chapter highlights a pathway to scalable, accurate, and patient-centric solutions for managing cardiomyopathies, paving the way for precision cardiology.