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Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings

Salah H Alahwany1,2, Omnia Kamel3, Amir Abdelghany4

  • 1Arrhythmogenic Cardiomyopathy Program, Vanderbilt Heart and Vascular Institute, Nashville, Tennessee.

Heart Rhythm O2
|October 31, 2025
PubMed

Insights

Inherited cardiomyopathies cause sudden cardiac death globally. Artificial intelligence (AI) can improve early detection and risk prediction, especially in underserved populations, by analyzing complex genetic and clinical data.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Genetics

Background:

  • Inherited cardiomyopathies are a major cause of sudden cardiac death, particularly in young individuals.
  • Current screening and risk stratification methods face challenges due to genetic complexity and diagnostic tool limitations.
  • There is a critical need for improved diagnostic and risk prediction strategies, especially in under-resourced areas.

Purpose of the Study:

  • To review the limitations of current inherited cardiomyopathy risk models.
  • To synthesize artificial intelligence (AI) applications for disease-specific diagnosis and risk stratification.
  • To explore AI's role in personalized care and risk prediction for underserved populations.

Main Methods:

  • Literature review of existing risk models and AI applications in inherited cardiomyopathies.
  • Synthesis of AI techniques including machine learning, deep learning, and natural language processing.
  • Evaluation of AI's potential impact on diagnostic accuracy and cost-effectiveness in low-resource settings.

Main Results:

  • AI demonstrates potential to analyze complex datasets and identify subtle disease patterns.
  • AI applications can enhance diagnostic accuracy and cost-effectiveness in inherited cardiomyopathies.
  • AI offers a framework for personalized risk prediction and improved care, particularly for underserved groups.

Conclusions:

  • Artificial intelligence holds significant promise for transforming the diagnosis and management of inherited cardiomyopathies.
  • AI can overcome limitations of current tools, improving risk stratification and personalized treatment.
  • Implementing AI solutions is crucial for advancing cardiac care equity in under-resourced regions.

Keywords:
Artificial intelligenceDeep learningDeveloping countriesInherited cardiomyopathyMachine learningRisk stratificationSudden cardiac deathUnder-resourced settingsVentricular arrhythmias

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