Researchers at Odin Vision, a spinout from University College London (UCL), have developed an artificial intelligence (AI) system that will leverage space technology to help doctors identify bowel cancer.

The cloud-based system detects and characterises polyps via analysis of live colonoscopy video. This is expected to facilitate early detection and treatment of the cancer.

It is being developed under the Early Diagnosis Real-Time Healthcare System for Cancer (EARTH SCAN) project, which will utilise satellite communications along with the data compression software commonly used to operate space missions.

This space technology is set to allow use of the system globally. The project is being supported by a £1m grant from the UK Space Agency, with support from NHS England and the European Space Agency, as part of an overall £5m grant awarded in June 2018 to mark the 70th birthday of the NHS.

“The EARTH SCAN project is an exciting opportunity to use satellite technology to bring this AI support to doctors in real time.”

Odin Vision CEO Peter Mountney said: “We are moving into a new era of healthcare where AI will support doctors to identify and diagnose cancer faster and more effectively.

“The EARTH SCAN project is an exciting opportunity to use satellite technology to bring this AI support to doctors in real time. Real time support means doctors can make immediate decisions regarding treatment and patients can receive the results of their scan straight away instead of waiting weeks.”

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A clinical trial is being planned to be conducted next year at University College Hospital (UCH) to test the AI system for detecting bowel cancer. Early diagnosis of the cancer is associated with 90% survival rate.

The UK Space Agency has also awarded £2m in funding to the University of Leicester, for the creation of a new mobile application intended to help patients with conditions that could be exacerbated by pollution, such as asthma.

The app will use data from Earth Observation satellites, which map regions with pollution, along with AI to suggest personalised exercise routes for these patients. It offers warnings at the local level to within 10m.