Machine learning to predict outcome from cardiac imaging.
Research type
Research Study
Full title
Applications of Deep Learning in Estimating Clinical Outcomes and Sudden Cardiac Death from Cardiac MRI Scans
IRAS ID
243023
Contact name
Nick Linton
Contact email
Sponsor organisation
Imperial College London
Duration of Study in the UK
5 years, 0 months, 1 days
Research summary
Some patients with heart problems are at risk of dying from heart rhythm disturbance, causing sudden cardiac death. The risk is higher, for example, in patients with a previous heart attack that has weakened the heart's pumping function. It is important to predict which patients are at substantially increased risk because this influences their treatment. For patients at risk, an implantable cardiac defibrillator (ICD) may be helpful.
ICDs can save a patient's life by providing an electric shock to the heart if there is a life-threatening arrhythmia. However, ICDs have disadvantages too: there are risks with implantation; the patient requires lifelong follow-up; and there is an ongoing risk of infection and device-related complications.
All patients who have an ICD have some form of cardiac imaging to view the heart and it's overall pumping function. This is a crude test. We want to look at patients who have had cardiac MRI and also look at their outcomes to see if a Machine-Learning computer algorithm could do better. Machine Learning is used to predict descriptors from large datasets – it is a technique used by Google, Facebook, and Netflix (to name a few).
We aim to create an approach to improve the treatment of patients. Firstly, the algorithm will be developed using our data and then (as a further project) we will collaborate with other centres to increase the amount of data used.
REC name
East Midlands - Leicester Central Research Ethics Committee
REC reference
18/EM/0341
Date of REC Opinion
5 Nov 2018
REC opinion
Favourable Opinion