Background to the Project
Coronavirus Disease 2019 (COVID-19) is highly contagious, and severe cases can lead to acute failure of the lungs, multiple organs and ultimately death. Chest X-Rays and CT scans provide valuable diagnostic and monitoring information that can complement the laboratory and clinical data. We have formed a Cambridge led collaboration to bring together clinicians from Addenbrookes and Royal Papworth Hospitals, many other hospitals worldwide (see details below) as well as imaging and AI specialists from the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge.
The diagnosis of COVID-19 can be confirmed by a laboratory test, however, the test has high false-negative rates and low sensitivity which leads to late diagnosis and treatment. Fast and accurate diagnosis of patients is incredibly important, as is prognostication for whether a patient is likely to recover, require intensive care unit (ICU) admission or intensive ventilation. These lead to better patient outcome and allow efficient resource allocation.
Utilising the routinely acquired X-Ray and CT images of the patient, along with their associated clinical and laboratory data, we will develop an AI algorithm that can accurately diagnose a patient with COVID-19 and at the same time prognosticate for their likely clinical outcome.
Planned Investigative Work
In this project, we propose to develop an open-source artificial intelligence tool that combines chest imaging data and clinical data to support the diagnosis and triaging for COVID-19 in the UK.