A multi-disciplinary team of researchers has developed a way to monitor the progression of movement disorders using motion capture technology and AI.
By combining human movement data gathered from wearable tech with a powerful new medical AI technology, the team were able to 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.
Senior and corresponding author of both papers published in Nature Medicine, Professor Aldo Faisal, from Imperial College London’s Departments of Bioengineering and Computing, 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.”
In the DMD-focused study, researchers trialed the body worn sensor suit in 21 children with DMD and 17 healthy age-matched controls.
In the FA study, teams worked with patients to identify key movement patterns and predict genetic markers of disease. Using this new AI technology, the team were able to use movement data to accurately predict the ‘switching off’ of the FA gene, measuring how active it was without the need to take any biological samples from patients.
Scientists also discovered that the new AI technique could significantly improve predictions of how individual patients’ disease would progress over six months. Such a precise prediction allows clinicians to run clinical trials more efficiently so that patients can access novel therapies quicker, and also help dose drugs more precisely.
Smaller numbers for future clinical trials
In the DMD study, researchers showed that this new technology could reduce the numbers of children required to detect if a novel treatment would be working to a quarter of those required with current methods.
Similarly, in the FA study, the researchers showed that they could achieve the same precision with 10 patients instead of over 160. This AI technology is especially powerful when studying rare diseases, when patient populations are smaller.
In addition, the technology allows to study patients across life-changing disease events such as loss of ambulation whereas current clinical trials target either ambulant or non-ambulant patient cohorts.
Image shows: Luchen Li, wearing the sensor suit, and Professor Aldo Faisal.