Tracking the Preclinical Progression of Transthyretin Amyloid Cardiomyopathy Using Artificial Intelligence-Enabled Electrocardiography and Echocardiography.

ABSTRACT:

Background and aims

The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale pre-clinical testing. Artificial intelligence (AI)-enabled transthoracic echocardiography (TTE) and electrocardiography (ECG) may provide a scalable strategy for pre-clinical monitoring.

Methods

This was a retrospective analysis of individuals referred for nuclear cardiac amyloid testing at Yale-New Haven Health System (YNHHS, internal cohort) and Houston Methodist Hospitals (HMH, external cohort). Deep learning models trained to discriminate ATTR-CM from age/sex-matched controls on TTE videos (AI-Echo) and ECG images (AI-ECG) were deployed to generate study-level ATTR-CM probabilities (0-100%). Longitudinal trends in AI-derived probabilities

SUBMITTER: Oikonomou EK 

PROVIDER: S-EPMC11383475 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature