Research


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, imaging scientists and AI specialists from Cambridge and throughout the world.

Clinical Need

The diagnosis of COVID-19 can be confirmed by a laboratory test, however, the test has high false-negative rates which leads to delayed diagnosis and treatment. Fast and accurate diagnosis of patients is incredibly important, as is prognostication of whether a patient is likely to recover, require intensive care unit (ICU) admission or intensive ventilation. These tools will assist clinicians to allow them to make more informed decisions, leading to better patient outcome and efficient resource allocation (important at times of stretched resources).

Planned Investigative Work

Utilising the routinely acquired X-Ray and CT images of the patient, along with their associated clinical and laboratory data, we are developing an open source AI-based tool that can accurately diagnose a patient with COVID-19 and at the same time prognosticate for their likely clinical outcome. This will deployed in the UK initially, with everything openly shared to our global partners.

Preparedness for Future Pandemics

It is clear that the imaging and machine learning community could have responded better to the pandemic if the data were available rapidly and the appropriate analysis were performed to develop algorithms quicker. We are writing the blueprint for how to respond to the next pandemic, asking the critical questions:

  1. Where are the bottlenecks in the analysis and how can these be mitigated against in a future pandemic?

  2. How can we rapidly develop prognostic and diagnostic algorithms for a brand new disease?

  3. What is the most efficient method for integrating the algorithms into the clinical workflow?

  4. How would we proceed differently to ensure rapid regulation of the algorithms as a medical device?

Machine learning solutions, with the right data and development, can provide diagnostic and prognostic algorithms within weeks. Next time around we must be better placed to harness this promise.