Efficient and Robust Assessment of Cardiovascular Disease Using Machine Learning and Ultrasound Imaging

Lead Research Organisation: Imperial College London
Department Name: Dept of Computing

Abstract

Heart disease is the number one killer in the world. Currently the best way of diagnosing heart disease and planning its treatment is to use a magnetic resonance imaging (MRI) scanner. However, MRI scanners are expensive and not typically used for scanning hearts in most UK hospitals. Therefore, the best diagnosis and treatment are not available to all patients. Currently the most common way of assessing heart disease is through the use of an ultrasound scanner. Although ultrasound has many advantages, it does not have such good image quality as MRI and so there are difficulties associated with its use in heart disease management. If the 'gold standard' quality of assessment from MRI could somehow be made feasible using ultrasound it would have great potential benefits for patients.

This is the aim of this project. We aim to use state-of-the-art machine learning techniques combined with rich multimodal imaging data to produce a computer model of heart disease and its associations with heart shape and motion. By incorporating MRI as well as ultrasound imaging data into the model we can exploit the power of MRI based only on ultrasound imaging. This would make possible a low cost and easy clinical pathway to the best care possible.

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