STEM for Britain Award for Clinical AI Researcher

Postdoctoral Research Associate, Dr Andrew Creagh, was announced as this year’s Engineering Bronze place winner at the annual STEM for Britain awards event.

Dr. Creagh was credited for his work developing smartphone-based technologies and artificial intelligence (AI) strategies to monitor disease progression for people with neurodegenerative diseases, such as multiple sclerosis.

The STEM for Britain Awards

The STEM for Britain awards, usually held at the Houses of Parliament, is a major scientific poster competition organised annually by the Parliamentary & Scientific Committee since 1997. The aim of the event is to give members of both Houses of Parliament an understanding into the research being undertaken by early-career researchers across UK universities.

Initially selected as one of the top 10 engineering finalists from 85 applicants, Andrew was invited to present his research at the online Royal Academy of Engineering sponsored event to MPs and academic experts from around the UK. The judging panel awarded the STEM for Britain bronze award to Andrew in recognition of the original research exhibited in his thesis, “The Development of Digital Biomarkers for Multiple Sclerosis from Remote Smartphone- and Smartwatch-Based Assessments”.

We apply advanced signal processing, machine- and deep-learning techniques to improve our understanding of diseases

Andrew is currently a Postdoctoral Research Associate at the Computational Health Informatics (CHI) lab, led by Professor David A. Clifton, at the Institute of Biomedical Engineering.

“Our group specialises in ‘AI for healthcare’, where we apply advanced signal processing, machine- and deep-learning techniques to improve our understanding of diseases, to develop better therapeutic strategies, and enhance patient care in the UK and the developing world”, he says. The CHI group has access to some of the world's largest, curated, anonymised healthcare datasets, and includes work with wearables and hospital data, across scales from the massively multivariate (including anonymised genomics) to the time-series data acquired from medical devices.

Developing Digital Biomarkers for Disease Progression

Andrew also undertook his doctoral degree at the department of Engineering Science in collaboration with multinational pharmaceuticals company F-Hoffmann La Roche, in order to develop novel methods for discovering biomarkers for neurodegenerative diseases using smartphones and smartwatches.

“Nowadays, everyone has a smartphone, and more and more people have smartwatches. These devices contain loads of different sensors which we can use to take measurements about a person’s daily life and health”, says Andrew, discussing his research. “We can then use these digital measurements to develop digital markers of disease: digital biomarkers”.

This is essentially giving clinicians an extra pair of eyes

The problem is that if you have a neurodegenerative or autoimmune disease, you might only see a clinician twice, or three times a year, Andrew points out. “This means we miss all the disease-related changes that occur when patients are not in the clinic. Instead, the idea is to try and measure a person’s disease symptoms using smartphones and smartwatches when they are at home”. Andrew adds, “this is essentially giving clinicians an extra pair of eyes” to measure aspects of a patient’s life they would normally be able to assess.

Clinical applications of machine learning (ML), often termed under the broader tag of artificial intelligence (AI), can act as powerful tools to learn complex and unseen digital patterns of disease. Andrew’s research utilises the latest deep learning algorithms to learn a patients’ disease severity from smartphone and smartwatch measurements, which he has shown can accurately monitor a patient’s MS symptoms over a 6-month period.