MACHINE LEARNING TO SIMPLIFY COMPLEX CORONARY SMALL VESSEL DIAGNOSTICS
Cardiac Small Vessel Disease (SVD) is a condition that often goes undetected, with patients suffering from symptoms such as chest pain and breathlessness due to the invisibility of small vessels on standard heart scans, resulting in thousands of patients being misdiagnosed every year. SVD is currently difficult to detect without invasive, expensive tests.
The team at University of Glasgow are pioneering a novel software application to tackle diagnosis. The invention uses a machine learning tool to process heart artery images to accurately identify SVD, streamlining diagnosis and offering a safer, faster, and more cost-effective solution. The target customer is specialists at cardiology units and hospital imaging departments. The software is also well-suited for digital diagnostics, medical imaging, and clinical research sectors, offering seamless integration with current platforms and workflows.
contact: innovation@glasgow.ac.uk
THE TEAM
An expert team from the School of Cardiovascular and Metabolic Health are driving this innovation and includes a blend of clinical, technical, and commercial acumen including expert support in model testing and data analysis. There is ongoing contribution from top-tier software engineering and infrastructure design expertise, ensuring the project’s technical robustness.
MEDTECH INNOVATION FUND SUPPORT
MedTech Innovation funding has enabled the team to move from academic research to a practical clinical tool, supporting collaboration with expert developers to create a regulatory-ready design, helping to build a scalable imaging and data infrastructure to collect and organise angiograms and physiology data from multiple sites. This stage has been critical in translating the machine learning algorithm into a real-world solution that clinicians can use.
FINAL OVERVIEW
The funding further facilitates the development of operational plans for future integration and licensing with collaborating organisations and industry partners. The team is poised to continue refining the tool and leveraging the new data infrastructure.
This software solution represents a leap forward in heart disease diagnosis, particularly for patients whose conditions are not characterised by blocked arteries. The innovation stands to improve patient care and alleviate the burden on healthcare systems like the NHS.