National COVID-19 Chest Image Database Collaboration

Research output: Other contributionpeer-review

Abstract

Collaboration Universities of Brighton, Oxford, Glasgow, Lincoln and Sheffield

Members of this collaboration were instrumental in winning first place in ‘Coronahackathon’ April 2020, for the development of Machine Learning (ML) and Artificial Intelligence (AI) to predict patients with SARS-CoV-2 virus using full blood count results. SARS-CoV-2 positive patients exhibit a characteristic change in different parameters measured in simple and rapid blood tests to a high accuracy, predicting the virus in regular wards (93-94%) and those in the community (80-86%).

Our project will validate these initial results and enable use in current hospital practice to screen patients and identify those needing full diagnosis for SARS-CoV-2. Expertise in chest images, blood science and modelling ML and AI will develop an innovative tool to upscale screening to identify individuals for full rt-PCR testing of the virus potentially up to one week earlier than rt-PCR, which will allow much faster release of the country (and the world) from lockdown, protection against future waves and future pandemics.
Original languageEnglish
Publication statusPublished - 13 Jan 2021

Keywords

  • COVID-19 pandemic

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