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MedTech Solutions

MedTech Solutions undertakes collaborative research with partners across Lancashire and beyond.

We support the design and development of medical devices and related interventions, provide insights into the efficacy and effectiveness of such solutions, and accelerates the pace at which these reach healthcare providers to bring benefits to patients and service users.

MedTech Solutions formed in June 2020 bringing together a collective of scientists, engineers and technicians from across the University to help solve problems faced by the NHS and the wider healthcare sector.

Our group includes experts in mechatronics and intelligent machines, additive manufacturing, design and development of medical devices, computer vision and machine learning, psychology, health sciences, and microbial interactions with hosts and surfaces. Our team has extensive experience and a successful track record of grant capture, assisting small and large companies, and working alongside the NHS and other healthcare partners.

Our membership currently includes experts from the fields of Medicine, Health Sciences, Engineering, Computing, Psychology, Enterprise and Innovation. Our primary focus is on product development, testing and evaluation to address important and emerging challenges across the healthcare sector. This work spans the innovation pathway, from the development of medical technology, through to its evaluation, approval and implementation.

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Engineering Innovation Centre

Med-Tech Solutions are embedded in the Engineering Innovation Centre (EIC) located on our Preston campus which brings together world-leading research, leading business minds and inspiring teaching in a spirit of collaboration and discovery.

Within the EIC our MedTech Solutions group offers a wide-ranging portfolio of industry engagement opportunities. When you work with us, you have access to specialist staff, state-of-the-art engineering facilities, industry leading technologies and the expertise of our researchers inside a first-class facility.

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Associated groups

Computer Vision, Machine Learning and Medical Image Computing (CVML)

The ongoing emphasis is on developing new vision and machine learning algorithms and their transfer to real-world applications. The particular areas of interest include: Bayesian methodology for data modelling, pattern recognition and tracking; statistical shape analysis; deformation modelling for model-based recognition, segmentation and registration; medical imaging; intelligent energy management; data mining; and applications of deep learning.

Allied Health Research unit (AHRu)

The Allied Health Research unit’s work includes research across the health care professions to help answer important and clinically relevant questions, with research spanning the innovation pathway from intervention development and evaluation through to implementation.

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