AI-driven study redefines right heart health assessment with novel predictive model

Jan. 4, 2024
Departure from traditional methods marks a significant advance in evaluating heart health, paving the way for more innovative tools and improved patient outcomes.

In a milestone study, researchers from the Icahn School of Medicine at Mount Sinai have harnessed the power of artificial intelligence (AI) to enhance the assessment of the heart’s right ventricle, which sends blood to the lungs.  

Conducted by a team using AI-enabled electrocardiogram (AI-ECG) analysis, the research demonstrates that electrocardiograms can effectively predict right-side heart issues, offering a simpler alternative to complex imaging technologies and potentially enhancing patient outcomes.

The findings were described in the December 29 online issue of Journal of the American Heart Association:

The study trained a deep-learning ECG (DL-ECG) model using harmonized data from 12-lead ECGs and cardiac magnetic resonance imaging (MRI) measurements. It was conducted on a large sample from the UK Biobank and validated at multiple health centers across the Mount Sinai Health System, measuring its accuracy in predicting heart conditions and its impact on patient survival rates.

The investigators say that while the use of artificial intelligence allows for more precise heart information from commonly available tools, it's in an early stage and doesn't replace advanced diagnostics. Further work is needed to ensure the tool's safety and correct applicability.

In addition, the study's predictions may vary across populations, relying on existing ECG and MRI data with inherent limitations. Its application in everyday clinical practice requires further exploration, cautioned the researchers.

Mount Sinai release on Newswise