Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy

ABSTRACT: Abstract

Aims

Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation.

Methods and results

We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage o

SUBMITTER:  

PROVIDER: S-EPMC9707995 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature