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Clinical AI Researchers Win IET Healthcare Technologies Award

Dr Andrew P. Creagh and Dr Lei Lu were credited for work developing artificial intelligence strategies for patient monitoring in healthcare settings

(L-R) Dr Andrew P. Creagh & Dr Lei Lu

Postdoctoral Research Associates Dr Andrew P. Creagh and Dr Lei Lu have been announced as this year’s winners of two prestigious Institution of Engineering and Technology (IET) Healthcare Technologies Awards for early-career researchers.

The IET’s annual awards encourage and support the work and research of academics and newly qualified professionals in healthcare, medical and biomedical engineering arenas. The Institute of Biomedical Engineering researchers were both recognised for their work developing artificial intelligence (AI) strategies to monitor patients in healthcare settings.

Dr Creagh received the IET William James Award for his work developing smartphone-based technologies and artificial intelligence (AI) strategies to monitor disease progression for people with neurodegenerative diseases, including a simple smartphone-based, at-home examination tool, named “Draw-a-Shape”, to help monitor Multiple Sclerosis symptoms.

Dr Lu’s IET J. A. Lodge Award relates to his research identifying data-driven measures to monitor the rehabilitation progress of stroke patients, derived from robotic-collected kinematic data. Sensors are used to capture a patient’s motion while they perform virtual gaming exercises. These kinematic measures are then used to identify clinically meaningful information to inform practitioners.

 

The IET Healthcare Technologies Awards

The IET J. A. Lodge Award, in memory of the late James Alec Lodge, acknowledges innovative “Electronic or Electrical Engineers working at the early stage of their careers (first five years) in the field of research and development within Biomedical Engineering”.

The IET William James Award, set up by Professor Christopher James in honour of his late father William James, is presented to an early-career researcher whose PhD work “has demonstrated the most potential to contribute towards the development and improvement of the Biomedical Engineering field”.

 

Developing Digital Biomarkers for Disease Progression

“Our research is trying to harness the power of digital consumer technologies, like the smartphone nearly all of us have in our pocket."

The IET William James Award was presented for Dr Andrew Creagh’s research helping to develop a simple smartphone-based, at-home examination tool, named “Draw-a-Shape”, which was the first published step in validating digital biomarkers for MS, from the landmark FLOODLIGHT proof-of-concept study.

Andrew explains: “Our research is trying to harness the power of digital consumer technologies, like the smartphone nearly all of us have in our pocket. These devices contain loads of different sensors, which we can use to monitor a patient’s daily life and health from their own home. Transforming these digital measurements, through machine learning, allows us to create digital markers of disease and disease progression”.

Simple smartphone-based assessments, such as a touch screen drawing test, can be used to measure disease symptoms when patients are not in the clinic. Credit: Dr Andrew Creagh.

“I’m delighted, and honoured, to be named as the recipient of this year’s IET William James Award. There is some incredibly exciting research that is being conducted across the UK in healthcare, and we are at the forefront of this innovation here at Oxford. I would also like to express my gratitude to Professor David Clifton, Prof. Maarten De Vos, F-Hoffmann La Roche, GSK, as well as all our collaborators and those who have mentored me along the way”.

 

Robotic-Assisted Stroke Patient Rehabilitation

"Evaluating the progress of rehabilitation strategies can help determine the effectiveness of selected treatments"

The IET J. A. Lodge Award recognises Dr Lei Lu’s work developing a robotic system to monitor the rehabilitation of stroke patients. "Evaluating the progress of rehabilitation strategies can help determine the effectiveness of selected treatments", Lei says. "We utilised data-driven techniques to create a number of robotic-derived kinematic markers, which provided clinically useful information to health practitioners for monitoring patient rehabilitation".

Robot-assisted Stroke Rehabilitation. A total of 30 sensors are used to capture a subject’s motion while they perform virtual gaming exercises. These kinematic measures are then used to identify clinically meaningful information to inform practitioners of stroke rehabilitation progress. Credit: Dr Lei Lu.  

Dr Lu adds, “I am truly honoured to receive the prestigious IET J. A. Lodge, [which] recognises my research in healthcare with machine learning. It is in particular a great encouragement for me to dedicate myself to creating innovative solutions to address healthcare challenges. I would like to thank Professor David A. Clifton and the Hong Kong Center for Cerebro-cardiovascular Health Engineering (COCHE), as well as all of my previous collaborators for their continuous help and support: Professor Ying Tan, Professor Denny Oetomo, Professor Iven Mareels, Dr. Marlena Klaic, Professor Mary P. Galea, Professor Fary Khan, Ms. Annie Oliver, and Dr. Erying Zhao”.

 

The Computational Health Informatics (CHI) Lab at Oxford University

“We use the latest in machine- and deep-learning techniques to improve our understanding of diseases, to develop better therapeutic strategies, and enhance patient care”

Dr Lu and Dr Creagh are both Postdoctoral Research Associates in the Computational Health Informatics (CHI) lab led by Professor David A. Clifton at the Institute of Biomedical Engineering in Oxford. The CHI lab specialises in the interface between machine learning or artificial intelligence and medicine, which could help tackle some of healthcare’s biggest problems.

“We use the latest in 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”, Andrew says, when discussing the work of the lab. “Through our amazing collaborators, in the NHS, in academia, in industry, and in hospitals around the world, our lab is able to become a small cog within the ground-breaking medical research being undertaken here at Oxford”.

The CHI team work with the some of the world’s largest, curated, and anonymised healthcare datasets. “Our speciality is time-series health data”, Dr Lei Lu explains. “These can be anything from wearables, to hospital data, or to genetics, across scales from the massively multivariate, to high-rate sensor data acquired from medical devices, such as your heart rate or blood pressure”.

 

Further information

Dr Andrew Creagh is currently a Postdoctoral Research Associate in the Computational Health Informatics (CHI) lab and a Junior Research Fellow at St Cross College, University of Oxford. He is also concurrently a GSK Postdoctoral Fellow in Digital Biomarkers and a visiting Postdoctoral Researcher at the Wearables Laboratory at the Big Data Institute (BDI), University of Oxford. An example of Dr Creagh’s research can be found at: https://doi.org/10.1088/1361-6579/ab8771.

Dr Andrew Creagh’ bio and research information: https://eng.ox.ac.uk/people/andrew-creagh/

Dr Lei Lu is currently a Postdoctoral Research Associate in the Computational Health Informatics (CHI). An example of Dr. Lu’s research can be found at: https://doi.org/10.1109/TBME.2020.3036095.

Dr Lei Lu’s bio and research information: https://eng.ox.ac.uk/people/lei-lu/