Summary: Combining new wearable technology and Artificial Intelligence, researchers are better able to track motion and monitor the progression of movement disorders.
A multi-disciplinary team of researchers has developed a way to monitor the progression of movement disorders using motion capture technology and AI.
In two ground-breaking studies, published in Nature Medicine, a cross-disciplinary team of AI and clinical researchers have shown that by combining human movement data gathered from wearable tech with a powerful new medical AI technology they are able to identify clear movement patterns, predict future disease progression and significantly increase the efficiency of clinical trials in two very different rare disorders, Duchenne muscular dystrophy (DMD) and Friedreich’s ataxia (FA).
DMD and FA are rare, degenerative, genetic diseases that affect movement and eventually lead to paralysis. There are currently no cures for either disease, but researchers hope that these results will significantly speed up the search for new treatments.
Tracking the progression of FA and DMD is normally done through intensive testing in a clinical setting. These papers offer a significantly more precise assessment that also increases the accuracy and objectivity of the data collected.
The researchers estimate that using these disease markers mean that significantly fewer patients are required to develop a new drug when compared to current methods. This is particularly important for rare diseases where it can be hard to identify suitable patients.
Scientists hope that as well as using the technology to monitor patients in clinical trials, it could also one day be used to monitor or diagnose a range of common diseases that affect movement behavior such as dementia, stroke and orthopedic conditions.
Senior and corresponding author of both papers, Professor Aldo Faisal, from Imperial College London’s Departments of Bioengineering and Computing, who is also Director of the UKRI Center for Doctoral Training in AI for Healthcare, and the Chair for Digital Health at the University of Bayreuth (Germany), and a UKRI Turing AI Fellowship holder, said, “Our approach gathers huge amounts of data from a person’s full-body movement—more than any neurologist will have the precision or time to observe in a patient.
“Our AI technology builds a digital twin of the patient and allows us to make unprecedented, precise predictions of how an individual patient’s disease will progress.
“We believe that the same AI technology working in two very different diseases, shows how promising it is to be applied to many diseases and help us to develop treatments for many more diseases even faster, cheaper and more precisely.”
The two papers highlight the work of a large collaboration of researchers and expertise, across AI technology, engineering, genetics and clinical specialties.
These include researchers at Imperial, the UKRI Center in AI for Healthcare, the MRC London Institute of Medical Sciences (MRC LMS), UCL Great Ormond Street Institute for Child Health (UCL GOS ICH), the NIHR Great Ormond Street Hospital Biomedical Research Center (NIHR GOSH BRC), Ataxia Center at UCL Queen Square Institute of Neurology, Great Ormond Street Hospital, the National Hospital for Neurology and Neurosurgery (UCLH and UCL/UCL BRC), the University of Bayreuth, the Gemelli Hospital in Rome, Italy, and NIHR Imperial College Research Facility.
Co-author of both studies Professor Richard Festenstein, from the MRC London Institute of Medical Sciences and Department of Brain Sciences at Imperial said, “Patients and families often want to know how their disease is progressing, and motion capture technology combined with AI could help to provide this information.
“We’re hoping that this research has the potential to transform clinical trials in rare movement disorders, as well as improve diagnosis and monitoring for patients above human performance levels.
