Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function

Cardiovascular disease is the number one cause of death globally and represents a huge burden on the healthcare systems of the world. Diagnosis and planning of treatment for cardiovascular disease is often difficult and sometimes requires an invasive procedure which can itself be risky for the patient. Therefore, there is a lot of interest in devising improved and noninvasive techniques for diagnosis and treatment planning.

Cardiovascular disease affects the ability of the heart to pump blood around the body. This ability is affected because the motion of the heart walls has been changed by the disease process to make the pumping action less efficient. Diagnosis and treatment planning for cardiovascular disease typically involves the use of imaging scanners such as ultrasound or magnetic resonance in an effort to evaluate the heart's motion and isolate the source of the problem. However, still in many cardiovascular applications the success rate of diagnosis and treatment planning is poor and patients suffer as a result.

The aim of this project is to use sophisticated imaging and motion analysis techniques to devise novel noninvasive biomarkers for cardiovascular disease. The project will use motion modelling techniques that have previously been applied to correct the 'problem' of motion, for example to reduce artefacts in acquired images where the organ being imaged was moving. These techniques will be adapted to analyse the nature of the motion and to extract clinically useful information from it. This motion-based information will be combined with other multimodal data, such as anatomical information, genetic information or clinical history, to produce comprehensive noninvasive biomarkers of cardiovascular function.

We will focus on two clinical exemplar applications. First, selection of patients for cardiac resynchronisation therapy (CRT). CRT is commonly used to treat heart failure but 30% of patients do not respond to the treatment and therefore undergo the invasive and risky procedure unnecessarily. We aim to devise biomarkers that can distinguish between patients that will respond to CRT and those that will not. The second application is the investigation of the effect of genetic variation on cardiac motion patterns. A large number of cardiovascular diseases are inherited. In several of them, such as left ventricular hypertrophy, many people exhibit no detectable symptoms until heart failure develops. Therefore, there is significant interest in discovering the mechanisms behind these conditions. We aim to devise biomarkers that can help us to understand the link between genetics and heart failure. Such an understanding would have the potential to result in improved screening and diagnosis of patients at genetic risk of heart failure.

The project is highly novel and has significant potential impact. As well as the two clinical exemplar applications mentioned above, if successful similar techniques could be applied to other cardiovascular diseases, resulting in improved diagnosis and treatment for a wide range of heart conditions.

One of the primary groups of beneficiaries of the project is patients with cardiovascular disease as well as their relatives and carers. Two patient groups will benefit directly from the proposed research: patients being considered for cardiac resynchronisation therapy (CRT) as well as patients at risk of developing heritable cardiovascular diseases. The secondary beneficiaries will be clinicians and nurses involved in the care of these patients as well as charities promoting cardiovascular health. In addition to benefits to healthcare providers, the research will also have impact on businesses that are developing innovative solutions to these healthcare challenges. Other beneficiaries of this work include researchers in medicine, life science and computer science.

Patients being considered for CRT will benefit from improved stratification, enabling a more informed selection of patients for this invasive procedure. Therefore, this patient group will benefit by reduced morbidity and mortality. As well as the short term goal of improving patient selection, in the longer term we believe that the proposed research could lead to information that could increase the number of patients who can be effectively treated by CRT, for example by optimising pacemaker lead placement. This could open up the possibility of effective treatment for heart failure to a larger group of patients.

The second group of patient beneficiaries are patients at risk of developing heritable cardiovascular diseases. The developed motion-based biomarkers will offer the potential for identification of at-risk patients for screening and early diagnosis, allowing for earlier and more effective treatment. In the longer term we expect also that other patients with cardiovascular diseases will benefit from the research through improved and more accurate diagnostics. Other potential applications include, but are not limited to, localisation of late activation regions for planning and monitoring treatment for left ventricular assynchrony, identifying regions of wall motion abnormalities following myocardial ischaemia and preclinical diagnosis of hypertrophic cardiomyopathy.

At the same time clinicians and the NHS will benefit by improved tools for diagnosis and treatment planning in cardiology. This will enable more efficient and targeted use of clinical resources. For example, in the case of CRT patients more efficient use will be made of clinicians' time and catheter laboratory time by avoiding performing CRT procedures on non-responders. In the case of patients with heritable cardiocascular diseases early diagnosis will enable earlier treatment and the reduction of complications associated with late diagnosis, thus also reducing impact on NHS resources. Finally, the research will strengthen the international position of UK healthcare and biomedical industries in the area of stratified medicine, especially in diagnostics.