Supporting the development and evaluation of neurosurgical technologies

We are pleased to announce our new clinical theme - neurosurgery, led by Mr Ryan Mathew, Associate Professor at the University of Leeds and an Honorary Consultant Neurosurgeon at Leeds Teaching Hospitals.

The aims of the neurosurgery theme are to evaluate and define the role of innovative and disruptive technologies on common pathologies within neurosurgery where there is potential impact for improving patient outcomes.

Indications of interest are brain tumours and degenerative spinal conditions. Technologies that are considered to have potential will be supported to progress to mixed methodology clinical trial evaluations, encompassing pilot, feasibility, and implementation. There will also be a focus on utilising technology in the context of advancing neurosurgical training and competence to enhance the quality of service provision.

Cross-cutting technologies of interest

Immersive Technologies

Immersive technologies (virtual and augmented reality) have been shown to engage people in their health and wellbeing. Neurosurgery has a long track record of being at the cutting edge of technology (examples include frameless stereotactic navigation, intra-op ultrasounds and MRI, robotics, real-time neurophysiology). It is therefore well placed to integrate and implement immersive technology into the peri-operative patient journey and neurosurgical training. The focus will be on patient education of complex neurosurgical concepts, pre- and intra-op navigation and surgical planning tools, post-op neuro-rehabilitation, and enhanced neurosurgical training using virtual and mixed reality environments to complement cadaveric and simulation teaching.

We are interested in:

• Collaborations to develop immersive technology training modules for a commonly performed neurosurgical procedure.
• Collaborations for live-streaming capability for surgical education at undergraduate and postgraduate level.
• Collaboration to develop a platform for immersive technology-based educational modules to complement cadaveric based neurosurgical courses.

Bespoke Patient Care

Brain tumours have clear individual and societal impacts (shortened life expectancy, loss of working years, loss of independence, decreased QoL). Each patient with a brain tumour is unique due to the nature of the disease on such an eloquent host organ. This inherent patient heterogeneity (coupled with intra-tumoural heterogeneity) means that a bespoke patient-facing approach is needed.

We are interested in:

• Technologies with the capability to predict brain tumour treatment response on an individual patient basis.
• Technologies that support health professionals allied to neurosurgery to conduct research into bespoke patient therapy regimens with the aim of pre-habilitation and tailored post-operative programmes, to enhance and maximise recovery after brain tumour and spinal surgery.
• Building a platform for research into spinal conditions that informs future medical device design and manufacture for treating chronic degenerative spinal conditions.
• ?Bespoke radiotherapy regimens informed by enhanced imaging modalities.?

Enhanced Detection of Neurosurgical Pathologies

Common neurosurgical pathologies such as brain tumours and chronic degenerative conditions benefit from enhanced imaging and detection, facilitating earlier diagnosis and better-informed management. In the case of infiltrating brain tumours, the ability to differentiate normal from diseased tissue in real-time can inform extent of resection and stop/go decisions intra-operatively. Real-time histology could also maximise the yield of diagnostic tissue and minimise the risk of a repeat procedure. Machine-learning algorithms can longitudinally detect brain tumour progression and inform prognosis and decision making for timing of intervention. Enhanced imaging of cervical degenerative pathology can inform choice of surgical procedure.

We are interested in:

• ?Building links with industry and cross-disciplinary computer scientists to develop machine learning pipelines for the early detection of brain tumours.
• Identifying industry partners with capacity and capability to collaborate on machine learning for big data (electronic health record and imaging) analysis to pilot prospective brain tumour progression.
• ?Performing preliminary analysis of enhanced imaging techniques to better characterise spinal degenerative pathology.
• ?Identifying training needs in surgeons to enable them to use real-time histology and enhanced imaging to effectively inform surgical decision making.

What can you do if you would like to get involved:

• Sign up to our mailing list for regular updates
• Sign up as a MIC Associate here
• Download our Translational Toolkit which has details of the type of support we can offer and what you can expect from a collaboration.
• Download the Surgical MIC Smartphone App and register your details for consideration for future collaborations.