Researchers have used artificial intelligence (AI) to predict the oxygen needs of COVID-19 patients on a global scale.

Addenbrooke’s Hospital (Cambridge, England) along with 20 other hospitals from across the world and AI technology company NVIDIA (Santa Clara, CA, USA) have built an AI tool to predict how much extra oxygen a COVID-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest X-rays and electronic health data from hospital patients with symptoms of COVID-19.

To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had ‘learned’ from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospitalized COVID-19 patients anywhere in the world. The study dubbed EXAM (for EMR CXR AI Model), was one of the largest, most diverse clinical federated learning studies to date. To check the accuracy of EXAM, it was tested out in a number of hospitals across five continents. The outcomes of around 10,000 COVID patients from across the world were analyzed in the study. The results showed it predicted the oxygen needed within 24 hours of a patient’s arrival in the emergency department, with a sensitivity of 95% and a specificity of over 88%.

“Federated learning has transformative power to bring AI innovation to the clinical workflow,” said Professor Fiona Gilbert, who led the study. “Our continued work with EXAM demonstrates that these kinds of global collaborations are repeatable and more efficient, so that we can meet clinicians’ needs to tackle complex health challenges and future epidemics.”

“Usually in AI development, when you create an algorithm on one hospital’s data, it doesn’t work well at any other hospital,” said Dr. Ittai Dayan, first author on the study. “By developing the EXAM model using federated learning and objective, multimodal data from different continents, we were able to build a generalizable model that can help frontline physicians worldwide.” 

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