Scientists and researchers working on medical imaging projects can apply for a grant to use Zegami, the Oxford University data visualisation spin-out, with preference given to those 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.
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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.
Largest collection of images of Covid-19 infected lungs
In developing its new platform, 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, CEO and Founder, 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. It is currently exploring new ideas and making new discoveries for 35 clients and counting, across an ever-growing variety of sectors. It is currently being used in the fight against cancer, to protect nature and to feed the world.