In a groundbreaking study, researchers from the Icahn School of Medicine at Mount Sinai have leveraged artificial intelligence (AI) to significantly improve the assessment of the heart’s right ventricle, responsible for pumping blood to the lungs.
This pioneering research, utilizing AI-enhanced electrocardiogram (AI-ECG) analysis, demonstrates that electrocardiograms can effectively predict issues with the right side of the heart.
This offers a simpler and potentially more effective alternative to complex imaging technologies like cardiac MRIs, potentially transforming patient outcomes.
The study’s findings, published in the Journal of the American Heart Association, represent a substantial advancement in cardiac health assessment.
Co-first author Son Q. Duong, MD, MS, emphasized the goal of developing a more efficient method to evaluate the health of the right ventricle, particularly its pumping ability and size.
The traditional techniques were inadequate, leading the team to explore the potential of AI-ECG analysis, especially beneficial for patients with congenital heart disease who often experience right ventricular issues.
The researchers trained a deep-learning ECG (DL-ECG) model using data from 12-lead ECGs and cardiac MRI measurements.
The study involved a significant sample from the UK Biobank and was validated across multiple Mount Sinai Health System centers. Its effectiveness in predicting heart conditions and influencing patient survival rates was a key focus.
Co-first author Akhil Vaid, MD, highlighted the uniqueness of this approach, noting its capability to predict aspects of heart health not easily quantifiable by other common tests, like heart ultrasounds.
However, the researchers caution that while AI offers more precise cardiac information from widely available tools, this innovation is still in its early stages and is not intended to replace advanced diagnostic methods.
Ensuring the tool’s safety and appropriate use requires further research and development.
The study’s predictions may also vary across different populations, as it relies on existing ECG and MRI data, which have inherent limitations. The application of this technology in routine clinical practice needs more exploration.
Senior author Girish Nadkarni, MD, sees this research as a significant step forward in assessing right heart health, envisioning a future where AI plays a central role in early and accurate diagnosis.
The study is notable for its application of AI to standard ECG data to predict right ventricular function and size numerically.
The research team plans further studies, including external validation of the DL-ECG models in diverse populations, to ensure broader applicability and confirm its clinical usefulness in various cardiac conditions like pulmonary hypertension, congenital heart disease, and cardiomyopathies.
This study paves the way for a new era in cardiac health assessment, where AI and digital technologies could revolutionize how heart diseases are diagnosed and treated.
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The research findings can be found in the Journal of the American Heart Association.
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