Zegami unveils new scheme for medical imaging researchers
Zegami's machine learning model uses X-rays of COVID-19 infected lungs, AI techniques and data visualisation tools to improve diagnosis and treatment of coronavirus patients
Zegami, the Oxford University data visualisation spin-out, has launched a new scheme where scientists and researchers from around the world who are working on medical imaging projects can apply for a grant to use its software.
And preference will be given to those scientists and researchers who are working on COVID-19 projects.
Zegami has developed a proof-of-concept machine learning model using X-rays of COVID-19 infected lungs, artificial intelligence techniques, and data visualisation tools that could help medical professionals identify coronavirus cases more effectively, but also potentially help provide a better idea of potential outcomes for patients, and even lead to more-effective treatments.
But, for the platform to reach its full potential, Zegami is trying to source a huge supply of COVID-19 X-rays and details on treatments used for patients and the outcomes.
In developing the solution, Zegami has initially used images of COVID-19 X-rays from the GitHub data initiative, which was launched by Joseph Paul Cohen, a postdoctoral fellow from Mila, University of Montreal.
He is looking to develop the world’s-largest collection of X-ray and CT images of COVID-19 infected lungs to enable automated diagnosis faster and more accurately.
Roger Noble, chief executive and founder of Zegami, said: “We are keen to support researchers and scientists working on medical imaging, and we invite anyone working in these areas – in particular COVID-19 - to apply for a grant to use our software. We aim to ensure that everyone can benefit from our solutions – commercial or not.”
Zegami launched out of Oxford University in 2016 and is currently exploring new ideas and making new discoveries for 35 clients across an ever-growing variety of sectors.