Increasing the efficiency of heart function assessment and diagnosis through echocardiography

Fellows: Courtney Irwin, Yoni Nachmany, Wiebke Toussaint, Dave Vanveen

Technical Mentor: Maren Eckhoff

Project Manager: Sara Guerreiro de Sousa

Project Partners: CIBERCV (Biomedical Research Networking Centers) and Cardiology Department of the University Hospital of Salamanca

Echocardiogram is the most routinely cardiac imaging test used in the diagnosis, management, and follow-up of patients with any suspected or known heart diseases. This imaging technique is particularly suited for the diagnosis of heart valve diseases and measurements of the size and function of the heart chambers. The main advantages of echocardiography above other imaging techniques are that it is fast (it takes between 10 and 20 minutes to do a full echocardiogram), cheap, not invasive, and portable, which makes it available even to patients that are not physically able to stay inside an MRI. However, the interpretation of these images has to be done by an expert cardiologist, which is a time consuming task and limits the number of echocardiograms that can be performed. 

The goal of this project is to improve heart health diagnosis from echocardiogram images using Artificial Intelligence (AI) and other machine learning techniques to automate some of the underlying tasks.