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.
In both the FA and DMD studies, all the data from the motion capture sensors was collected and fed into the AI technology to create individual avatars and analyze movements. This vast data set and powerful computing tool allowed researchers to define key movement fingerprints seen in children with DMD as well as adults with FA, that were different in the control group. Many of these AI-based movement patterns had not been described clinically before in either DMD or FA.
Scientists also discovered that the new AI technique could significantly improve predictions of how individual patients’ disease would progress over six months compared to current gold-standard assessments. Such a precise prediction allows to run clinical trials more efficiently so that patients can access novel therapies quicker, and also help dose drugs more precisely.
The new technology could help researchers carry out clinical trials of conditions that affect movement more quickly and accurately. 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.
The AI technology used 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.
The work is a result of a large collaboration of researchers and expertise, across AI technology, engineering, genetics and clinical specialties. These include researchers at Imperial College’s Department of Bioengineering and Department of Computing, the Medical Research Council London Institute of Medical Sciences, the U.K. Research and Innovation Centre in AI for Healthcare, University College London (UCL), Great Ormond Street Institute for Child Health, the National Institute for Health and Care Research (NIHR) Great Ormond Street Hospital Biomedical Research Centre (BRC), Imperial College London, Ataxia Centre at UCL Queen Square Institute of Neurology, Great Ormond Street Hospital the National Hospital for Neurology and Neurosurgery, the National Hospital for Neurology and Neurosurgery, the University of Bayreuth in Germany and the Gemelli Hospital in Rome, Italy.
The research was funded by a UKRI Turing AI Fellowship to Professor Aldo Faisal, NIHR Imperial College Biomedical Research Centre, the MRC London Institute of Medical Sciences, the Duchenne Research Fund, the NIHR Great Ormond Street Hospital BRC, the UCL/UCLH BRC, and the U.K. Medical Research Council.