Wearable tech, AI and clinical teams combine to change the face of clinical trial monitoring

A multi-disciplinary team of researchers, involving several UCLH and UCL clinicians and scientists, 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, Friedreich's ataxia (FA) and Duchenne muscular dystrophy (DMD).

Tracking the progression of DMD and FA 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.

FA and DMD 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.

The researchers estimate that using these disease markers means 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 behaviour such as dementia, stroke and orthopaedic conditions.

Movement fingerprints – the trials in detail

In the FA study, teams at the UCL Ataxia Centre, UCL Queen Square Institute of Neurology, and Imperial College London, worked with patients to identify key movement patterns and predict genetic markers of disease. They were able to administer a rating scale to determine level of disability of ataxia SARA and functional assessments like walking, hand/arms movements (SCAFI) in nine FA patients and matching controls. The results of these validated clinical assessments were then compared with the one obtained from using the novel technology on the same patients and controls. The latter showing more sensitivity in predicting disease progression.

FA is the most common inherited ataxia and is caused by the decrease of a protein called frataxin due to a genetic mutation. The mutation “switches off” the gene that variably reduces the level of frataxin in patients. Using this new AI technology, the team were able to use movement data to accurately predict the level of frataxin that differs in each patient, without the need to take biological samples. The two standard clinical assessments, SARA and SCAFI, failed to predict the frataxin in patients.

In the DMD-focused study, researchers and clinicians at UCL Great Ormond Street Institute of Child Health (UCL GOS ICH), Imperial College London and Great Ormond Street Hospital, trialled the body worn sensor suit in 21 children with DMD and 17 healthy age-matched controls. The children wore the sensors while carrying out standard clinical assessments (like the 6-minute walk test) as well as going about their everyday activities like having lunch or playing.

In both studies, all the data from the sensors was collected and fed into the AI technology to create individual avatars and analyse 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.

Researchers also discovered that the new AI technique could also 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.

Co-author Professor Paola Giunti, Head of UCL Ataxia Centre, Queen Square Institute of Neurology, and Honorary Consultant at the National Hospital for Neurology and Neurosurgery, UCLH, said: “We are thrilled with the results of this project that showed how AI approaches are certainly superior in capturing progression of the disease in a rare disease like Friedreich’s ataxia. With this novel approach we can revolutionise clinical trial design for new drugs and monitor the effects of already existing drugs with an accuracy that was unknown with previous methods.”

Professor Giunti, who is supported by the NIHR UCLH Biomedical Research Centre, added: “The large number of FA patients who were very well characterised both clinically and genetically at the Ataxia Centre UCL Queen Square Institute of Neurology in addition to our crucial input on the clinical protocol has made the project possible. We are also grateful to all our patients who participated in this project.”

Dr Suran Nethisinghe, the UCL Ataxia Centre, UCL Queen Square Institute of Neurology, said; “I’m delighted to have been involved in this exciting study, for which I characterised the genetic mutation (a GAA repeat expansion) and disease modifier that is responsible for the patients’ Friedreich’s ataxia. This is a very rare condition, but the Ataxia Centre participated in a natural history study clinically and genetically characterising more than 300 patients, which enabled us to select a very homogenous group of ambulatory patients to validate the sensor and AI tool. This was essential to the success of the study, which will have huge implications for future clinical trials.”

Smaller numbers for future clinical trials

This new way of analysing full-body movement measurements provide clinical teams with clear disease markers and progression predictions. These are invaluable tools during clinical trials to measure the benefits of new treatments.

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.

Similarly, in the FA study, the researchers showed that they could achieve the same precision with 10 of 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.