Professor Zulf Ali explains how Teesside University and TWI are creating advanced research and technology capability to improve our quality of life
In February 2017, Teesside University established the Healthcare Innovation Centre (HIC) in collaboration with TWI, a leading independent research and technology organisation. The centre’s focus is on the development of new healthcare technologies. It works collaboratively with end-users, industrial and practice partners to support the adoption of interventions, tools and therapies for health and social care, to improve quality of life and create economic growth.
The centre draws on the existing strengths of both the University and TWI and the end goal is to create advanced research and technology capability that is adopted by industry, thus makes a significant impact on people’s lives and wellbeing. The work the team is undertaking spans physical interventions (assistive technologies and prostheses), smart systems and advanced therapies (diagnostics and bioprocessing) and digital health (AI, machine learning, augmented reality and digital twins). These areas are underpinned by key capabilities in advanced manufacturing, advanced materials, disruptive sensing systems, ICT, medical devices and photonics. The following current (‘live’) projects demonstrate the centre’s expertise.
iChair: AI-based healthcare technology for elderly people
This project aims to create a modular telemedicine smart wheelchair that enables elderly and/or disabled patients to maintain independence but remain connected. The system records and transmits information related to the person’s physical location, status parameters as well as vital bio-signs in real time.
The autonomous wheelchair uses a navigation and positioning system to record the location of the start point and running path. Users are able to override the moving path and the system has the ability to recalculate the route back to the destination. Data from sensors attached to the wheelchair of multi-parameter vital signs – including oxygen saturation, blood pressure, respiration and body temperature – is collected, compressed and uploaded to the cloud for viewing.
The data is analysed in real time using AI algorithms to diagnose any associated conditions related to the wheelchair user. With the help of autonomous driving, users improve and maintain their independence, functional capacity, health status and quality of life as well as preserving their physical, cognitive, mental and social wellbeing.
iChair will realise real-time mobile monitoring, assessment and intelligent early warning of the physical condition of the elderly based on an AI monitoring and warning system. It will help save caregivers’ time and expense, while also reducing medical intervention.
IntelliScan: enhanced AI-based breast MRI interpretation healthcare technology
This project is developing an AI-based breast MRI scanning system for use as a highly efficient diagnostic decision-support tool for breast radiologists, which will help them to make a diagnosis faster and more accurately.
Breast cancer is the most common form of cancer worldwide and a major healthcare challenge. In the UK, one in seven women will be diagnosed with breast cancer in their lifetime, with 19% of breast cancers affecting women under 50. Magnetic resonance imaging (MRI) of the breast has become widely used for staging of cancer, solving unclear presentations and screening of high-risk groups. MRI can provide detailed information about the structure of breast tissues and the nature and size of tumours, providing a large volume of information to radiologists. Interpreting and reporting breast MRI is very time-consuming and turnaround times can be up to several weeks in the NHS.
To aid radiologists with diagnosis, the IntelliScan project has developed a fully automated solution for breast radiologists. Researchers have been able to develop a detection algorithm that uses AI to localise the breast lesion(s) and provide further diagnostic information. The algorithm can then predict a probability of malignancy. Up to now, the algorithm has been trained with thousands of images from over 200 patients, and the current precision is 95%.
This will provide a new innovative digital health solution that will disrupt the existing market by changing the way that breast cancer MRI scans are analysed, reported and actioned.
Advancing prosthetic sockets for amputees
In two projects the team is developing a new technique and procedure for rapid design and fabrication of well-fitting prosthetic sockets for above-knee amputees.
The current practice for socket design is highly dependent on the experience of the prosthetist, and sockets are designed without access to comprehensive information related to the comfort of an amputee, such as pressure. As a result, patients are required to visit a prosthetics clinic multiple times, during which multiple check sockets are fabricated for trial and error. This process is both costly and time-consuming and yet more than a third of amputees still reject their prostheses or show a rather low satisfaction level due to comfort issues. This is mainly due to socket-related issues, such as poor comfort, reduced biomechanical functionality and hampered control.
The QuickFit project is using smart QTSS materials to develop printable sensors that can be incorporated within a physical socket (check socket) to change the existing ‘feel and touch’ approach for the development of a good-fit socket that can be designed and fabricated in one day with enhanced comfort for the patients. A 3D model of the residual limb from digital images and a reconstruction algorithm is used to create the digital 3D model to produce the check socket. The sensors provide pressure distribution data under varying conditions within the socket to fabricate the final definitive socket.
The SocketSense project integrates advanced sensing, AI methods, embedded electronics and cloud computing to allow manufacturing of a comfortable socket system that is tailored to patients’ needs. This is achieved through real-time monitoring of residual limb tissues evolvement by collecting data through embedded sensors, biomechanical modelling, CAD/CAM and additive manufacturing. The prosthetic socket will be for all lower limb amputees and for patients to wear in everyday life, and should allow the prosthetist to achieve a good-fit socket within the same day and whenever the patient requires a new one.
Sepsis Point-of-Care diagnostic system
Sepsis is a life-threating illness that can arise unpredictably and progress very quickly. It is caused by an overwhelming immune response to infection. It is more common than heart attacks and is a leading cause of avoidable death.
Each year in the UK, between 44,000 and 68,000 people die of sepsis. It is difficult to identify and every hour that the sepsis is undiagnosed increases the risk of death by 6-10%. Early diagnosis is important so that treatment can start as quickly as possible and the number of deaths be reduced.
The team has developed a low cost and portable instrument with a highly sensitive cavity enhanced absorption (CEA) detection and a disposable cartridge. A finger-prick of a patient’s blood is placed in the cartridge which is then placed into the instrument and a number of key biomarkers are measured that can give information about the septic patient’s status.
Further development of the instrument will allow automated measurements so that it is easy to use and could be available in a GPs surgery, within an ambulance or within an emergency department, making it easier to identify sepsis earlier and provide faster treatment.
Microbioreactor: novel healthcare technology for miniaturised bioprocessing
Optimisation of bioprocesses relies on approaches that are either labour intensive or require expensive robotic systems. The centre has developed a low-cost 3D printed microbioreactor with integrated optical sensing (pH, oxygen and cell density).
A pressurised fluid driving system was used to control flow rates down to 0.7 μL/min and complex fluidic operations could be performed using off-chip fluidic switching from four reservoirs using solenoid valves. Oxygen was transferred from a headspace via a gas-permeable membrane and mixing is achieved using a small stirrer. The microbioreactor has been demonstrated for batch and continuous cultivations for optimisation of recombinant protein production with different feeds with good reproducibility shown between the cultivation of E. coli within the microbioreactor and a 2L bench scale bioreactor.
The developed system could be used in different applications including within synthetic biology, cell and gene therapy and organ-on-chips.
Over the past three years, the team has created a UK centre of excellence in healthcare technologies. The HIC is supporting companies for prototype development within the Tees Valley Region through the SME Innovation Accelerator, which is funded by the European Regional Development Fund (ERDF). Nationally, the HIC is supporting companies in healthcare technologies as an innovation pathway partner for the Academic Health Science Network for the North East and North Cumbria. Whilst the research is already delivering industrial focus and impact, as a valuable by-product the centre is also developing the next generation of researchers in healthcare innovation.
Professor Zulf Ali is director of the Healthcare Innovation Partnership, HIC
