CARDIOVASCULAR MEDTECH SECTOR TO ENJOY STRONG GROWTH BUT IT NEEDS A BIGGER FOCUS ON DATA AND REAL-WORLD EVIDENCE
Zegami, the Oxford University data visualisation spin-out, says the growing prevalence of heart disease and a favourable funding environment for cardiovascular medicine will see the devices market that serves this sector grow by between 3% and 6% per annum over the next five years. However, it warns that as the cardiovascular devices market becomes more crowded and competitive, and as healthcare budgets come under greater pressure, hospitals and healthcare professional increasingly want more data and research to show their effectiveness before they agree to engage with providers.
Given this, Zegami says the cardiovascular market is one of the most exciting and offers some of the greatest opportunities for the data visualisation sector.
Roger Noble, CEO and founder of Zegami said: “Medtech companies operating in the cardiovascular disease market need to place a greater focus on outcomes measurement and end-to-end evidence. Increasingly, they need to develop partnerships for data creation, capture, sharing, and analytics of this information to provide real-world evidence (RWE) and demonstrate value beyond traditional safety and efficacy points in order persuade hospitals and healthcare centres to engage with them.
“Also, medtech manufacturers of cardiovascular devices face the growing threat of commoditization and it is now more important than ever to deliver differentiated and high-impact solutions. They not only need to demonstrate product performance they also need to provide greater evidence that their solutions offer both clinical and economic value as healthcare budgets come under greater financial strain than ever before.
“Given all of this, the cardiovascular sector is one of the most exciting of all for the data visualisation market.”
Zegami is already operating in the cardiovascular sector. For example, it is working with Professor Paul Lesson at Cardiovascular Clinical Research Facility (CCRF) at Oxford University to help improve its diagnosis of cardiovascular diseases such as coronary heart and aortic diseases. The aim is to identify trends for early detection of disease by applying advanced data visualisation and analysis techniques to echocardiogram data.
Zegami is a data visualisation platform that allows you to easily explore large image datasets and unlock powerful insight by combining advanced analysis tools with a unique visualisation interface. The Zegami platform enables users to rapidly analyse, categorise, label and clean large image datasets, invaluable for many applications including training machine learning. It also creates insightful visualisations from the widest range of structured and unstructured data in business and scientific research.
Zegami and the medical sector
Zegami believes that over the next few years, the medical professional will be one of the biggest adopters of data visualisation tools, and it’s already working on several projects in this sector. They include:
· A project collaborating with Professor Jim Hughes at the MRC Weatherall Institute for Molecular Medicines (MRC WIMM) Centre for Computational Biology to help clean its data and assist with the training of its machine learning models, specifically around developing a better understanding of which proteins in genes bind, and where they do this in the genome.
· After winning Cancer Research UK’s Early Cancer Detection Sandpit Challenge in 2019, Zegami is currently working with Professor Barbara Braden (Oxford University), Dr Xiohang Gao (Middlesex University) and Dr Wei Pan (Herriot Watt University) to develop a real-time detection system for oesophageal cancers. These cancers are mainly detected by endoscopy, and are only usually noticed when they have reached an advanced stage and treatment is less effective and patient prognosis is poor.
· Working with Professor Paul Lesson at Cardiovascular Clinical Research Facility (CCRF) at Oxford University to help improve its diagnosis of cardiovascular diseases such as coronary heart and aortic diseases. The aim is to identify trends for early detection of disease by applying advanced data visualisation and analysis techniques to echocardiogram data.