Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Hypertrophic Cardiomyopathy (HCM) poses diagnostic and prognostic challenges due to its varied presentation.
• Traditional Electrocardiography (ECG) methods have limitations in fully characterizing HCM.
• The integration of advanced computational techniques is needed to improve HCM patient outcomes.

Purpose of the Study:

• To review the current applications of Artificial Intelligence (AI) and Machine Learning (ML) in analyzing Electrocardiography (ECG) for Hypertrophic Cardiomyopathy (HCM).
• To highlight the potential of AI/ML in enhancing HCM diagnosis, risk stratification, and management strategies.
• To discuss the advancements in AI methodologies, particularly Deep Learning (DL), for ECG interpretation in HCM.

Main Methods:

• Review of current literature on AI/ML applications in ECG analysis for HCM.
• Focus on Deep Learning (DL) models, including convolutional neural networks.
• Integration of clinical and imaging data with AI models for comprehensive risk assessment.

Main Results:

• AI/ML models demonstrate high accuracy in identifying HCM from ECGs, often surpassing traditional methods.
• AI excels in distinguishing HCM from other cardiac conditions, even with normal ECG findings.
• AI models show promise in predicting adverse events like sudden cardiac death, atrial fibrillation, and heart failure.

Conclusions:

• AI-driven ECG analysis offers a transformative approach to HCM diagnosis and prognosis.
• Personalized treatment strategies can be developed based on AI-powered risk stratification.
• Addressing data limitations is crucial for advancing the generalizability and clinical adoption of AI in HCM care.