Can artificial intelligence transform cardiomyopathy diagnosis and risk stratification in the Philippines?
Abstract
I read with great interest the article by Alahwany et al., which provides a rigorous synthesis of how artificial intelligence (AI) can transform the diagnosis and management of inherited cardiomyopathies in low-resource settings [1]. The integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) offers a unified framework for screening and prediction in hypertrophic (HCM), dilated (DCM), and arrhythmogenic cardiomyopathies (ACM). The reported diagnostic accuracy of AI-enhanced electrocardiograms (ECG) and point-of-care imaging, achieving sensitivities above 97% for high-risk HCM features, underscores their potential for widespread clinical use [1].
Fields of Study & Subjects
Cardiovascular Effects of Exercise
Cardiomyopathy and Myosin Studies
Cardiac electrophysiology and arrhythmias
Publication details
- Journal / source
- Heart Rhythm O2
- Volume
- 7
- Issue
- 3
- Pages
- 644–644
- Publication date
- 9 January 2026
- Language
- EN