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

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.

The primary beneficiaries of the proposed research will be patients with cardiovascular diseases. The secondary beneficiaries include clinicians, in particular cardiologists, involved in the care of patients with cardiovascular diseases. In addition, the NHS and healthcare industries in general are also likely to benefit from this research. Other secondary beneficiaries include patients' families, carers and employers.

One of the primary outcomes of the project is intended to be the provision of 'gold standard' diagnosis and treatment for heart disease based purely on ultrasound data. This would widen access to high quality care and potentially result in earlier diagnosis and better patient outcomes. Therefore, in the short to mid term, patients being treated for left ventricular arrythmia will benefit from improved ultrasound-based treatment for their condition. The NHS and healthcare industries are likely to benefit from the reduced costs resulting from the use of ultrasound rather than the more expensive magnetic resonance imaging. Patients' families and carers will benefit from the improved health of their loved ones, whilst patients' employers will benefit from improved productivity arising from the better health of their workforce. In addition, the deeper insight into ventricular remodelling gained by the research performed in this project is likely to result in a longer term benefit to patients with a wider range of cardiovascular diseases, as a result of the development of new and improved treatments and diagnostic techniques.