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UK funding (£1,822,550): GEMINI-OS : platform informatics for data-driven neuromodulation Ukri1 Dec 2022 UK Research and Innovation, United Kingdom
Overview
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GEMINI-OS : platform informatics for data-driven neuromodulation
| Abstract | Deep Brain Stimulation (DBS) is an established treatment for advanced Parkinson's disease (PD) and tremor which may significantly improve their quality of life. PD is a progressive disease for which there are symptomatic treatments but no cure. In the advanced stages, medications can fail to control symptoms and commonly cause movement and non-movement related side effects. At this stage, people with PD can be referred to a DBS centre where a team of experts, including a neurologist and a brain surgeon, conduct extensive assessments before potentially offering surgery. While DBS can be a life-changing treatment, up to 1 in 4 patients fail to benefit from DBS and some even worsen following surgery. Conversely, some patients who would benefit from it are not offered treatment. The main reason for such failure or inaccessibility to treatment is due to the incorrect selection process for this powerful therapy. Whilst technological improvements in the devices have massively improved the delivery of DBS treatment over the last 20 years, the ability to select the best candidates has remained static. The selection process is based on criteria established more than 30 years ago and is largely informed by the outcomes, experience and knowledge by an individual centre. Thus, the decision to undertake DBS surgery is based on manual clinical evaluation of all the data which relies on the skills and experience of each individual team at the different centres, leading to high levels of variability. This project will develop a new clinical tool which will facilitate and improve patient selection for DBS. We will develop a novel platform that combines data from before and after surgery to determine what factors predict a good or poor outcome from surgery. Providing a data-driven, objective tool for patient selection, will improve the process by: 1) reducing the number of people who will deteriorate following surgery; 2) increasing the number of people who are offered treatment, including those in the earlier stages; 3) giving clinicians greater confidence in the selection process. This platform will also allow the teams to draw on the experience from all of the centres using the platform, rather than just their own. Currently, only 500 people in the UK meet the clinical eligibility due to the narrow eligibility criteria although many more patients are thought to potentially benefit. This platform would give greater confidence to widen access and allow more patients to benefit. |
| Category | Collaborative R&D |
| Reference | 10036282 |
| Status | Active |
| Funded period start | 01/12/2022 |
| Funded period end | 31/03/2026 |
| Funded value | £1,822,550.00 |
| Source | https://gtr.ukri.org/projects?ref=10036282 |
Participating Organisations
| MACHINE MEDICINE TECHNOLOGIES LIMITED |
£949,350.00 |
| ST GEORGE'S UNIVERSITY OF LONDON |
£493,101.00 |
| UNIVERSITY OF NOTTINGHAM |
£380,099.00 |
The filing refers to a past date, and does not necessarily reflect the current state. The current state is available on the following page: Machine Medicine Technologies Ltd., London.
