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AI-powered diagnosis to improve heart and lung health
The use of artificial intelligence (AI) in healthcare is growing rapidly. There is greater potential than ever before for modernising medicine through automated computer techniques – and that’s why we’re delighted to announce four new awards, for researchers who are harnessing AI to improve cardiovascular and respiratory disease diagnostics.
Cardiovascular and respiratory diseases are leading causes of death, both in the UK and globally. Although large-scale investments have been made in heart and lung research across the UK, there are still some critical areas of need within these disciplines.
In recent years, medical researchers and practitioners have recorded an enormous amount of healthcare data, from high-resolution imaging to electronic health records. There is incredible potential to use AI to process this data in meaningful and efficient ways, and apply these findings to the way we diagnose and treat various heart and lung conditions. AI methods could revolutionise diagnostic processes in these fields, helping to analyse complex images and detect key clinical features efficiently and accurately.
Through our new fellowship scheme, we are investing £1.2million to enhance the integration of AI into these medical fields, and ultimately, improve outcomes for patients with serious heart and lung conditions.
Learn more about our newly awarded projects below:
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Using AI to diagnose rare respiratory diseases
Primary Ciliary Dyskinesia (PCD) is an inherited disorder that impairs cilia – tiny, hair-like projections that line the lungs, and move microbes and debris out of the airways. When the cilia are impaired, this can cause mucus build up, infections, inflammation, and lung damage.
It is critical to diagnose this condition early, for treatment and lung preservation, but this is often difficult due to low awareness and the need for specialist expertise.
Dr Mathieu Bottier at Guy’s and St Thomas’ NHS Foundation Trust will develop AI models to enhance the analysis of advanced microscopy images that examine cilia.
Dr Bottier will use various AI tools to improve diagnostics of PCD by removing subjectivity in the analysis of cilia, thereby accelerating diagnostic results. This will enable more clinicians to detect the condition and expand availability of the diagnosis platform across UK health centres.
Using computer learning to detect and treat poor heart function in children
Several babies born with heart abnormalities require open-heart surgery. This is a serious procedure that bruises the heart, making it harder to pump blood and oxygen to other organs, and in turn, causing damage. It is difficult to predict if a child will develop organ damage after surgery, and there remains uncertainty around the treatments and medications to use to support heart function.
Dr Timothy Dawes at the Great Ormond Street Hospital for Children NHS Foundation Trust will apply advanced computer learning techniques to help predict poor heart function risk factors following open-heart surgery. He will also work to identify the most effective treatments for boosting recovery.
Improving asthma diagnosis with smart tools
Despite its potential, allergy testing is rarely used in medical practice to diagnose or manage asthma. However, researchers have developed a promising newer method that measures allergy antibodies to over 100 specific allergen components. This method could produce highly detailed information, but interpreting these complex results remains a challenge.
Dr Sara Fontanella at Imperial College London has previously found that interconnections across these molecules can predict asthma and distinguish its severity. Now, she will use AI tools to help doctors and patients identify clinically important allergy profiles, to support more accurate asthma diagnoses and help predict which children are at risk.
Using AI to identify successful pacemaker treatments
Pacemakers are implanted devices that prevent slow heart rates by stimulating heart muscle. A new type of pacemaker technology, conduction system pacing (CSP), stimulates natural electrical pathways of the heart – allowing pacemakers to produce stronger, more coordinated heartbeats.
While CSP could revolutionise pacemakers and better prevent heart failure, it is currently limited to specialist centres, meaning that many patients don’t have access to it.
Dr Ahran Arnold at Imperial College London will develop an AI algorithm to analyse heart traces from ECGs and identify successful CSP. The AI tool will enable healthcare professionals who are less familiar with the new technology to adopt the technique, expanding patient access.
