fairfolio
Saved Projects

Leveraging AI to Assist Cardiovascular Disease Diagnosis

ISEF · 2023 Translational Medical Science

Thomas, Ethan · Ransom Everglades School

Overview

One of the most important metrics of cardiac health is ejection fraction. EF is the percentage of blood pumped out of the left ventricle each time the heart contracts and is most commonly analyzed using echocardiogram videos. Traditional interpretation of echocardiograms is expensive and time consuming. Automated prediction of ejection fraction would not only reduce expense and improve efficiency, but improve accuracy by reducing variability among doctors' interpretations. Variability is reported to be as high as 14%. In order to solve this problem, I proposed a novel deep learning model to enhance cardiac imaging analysis. The model leverages the advantages of both CNNs and Transformer encoders. The mean absolute error achieved by the model was 6.75. When embedded directly into an echocardiogram ultrasound machine, this tool will provide accurate, real-time results to assist cardiologists in making proper diagnoses. This will ultimately expedite life-saving procedures and eliminate unnecessary interventions, resulting in a higher quality of life for patients.

Competition history

Resources

Related projects

Closest projects by meaning, across every fair and year in the corpus.

Browse more like this

Source: Regeneron International Science and Engineering Fair

Save projects to your library

Sign in with Google to keep track of projects you find interesting, organized into folders. An account also raises your daily allowance for “Has this been done?”, and lets you create a key for the MCP server with a much higher limit than anonymous use. Browsing stays public.

Continue with Google