With communities across the nation experiencing a wave of COVID-19 infections, clinicians need effective tools that will enable them to aggressively and accurately treat each patient based on their specific disease presentation, health history, and medical risks.
In research recently published online in Medical Image Analysis, a team of engineers demonstrated how a new algorithm they developed was able to successfully predict whether or not a COVID-19 patient would need ICU intervention. This artificial intelligence-based approach could be a valuable tool in determining a proper course of treatment for individual patients.
The research team, led by Pingkun Yan, an assistant professor of biomedical engineering at Rensselaer Polytechnic Institute, developed this method by combining chest computed tomography (CT) images that assess the severity of a patient’s lung infection with non-imaging data, such as demographic information, vital signs, and laboratory blood test results. By combining these data points, the algorithm is able to predict patient outcomes, specifically whether or not a patient will need ICU intervention.
The algorithm was tested on datasets collected from a total of 295 patients from three different hospitals—one in the United States, one in Iran, and one in Italy. Researchers were able to compare the algorithm’s predictions to what kind of treatment a patient actually ended up needing.
“As a practitioner of AI, I do believe in its power,” said Yan, who is a member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer. “It really enables us to analyze a large quantity of data and also extract the features that may not be that obvious to the human eye.”
This development is the result of research supported by a recent National Institutes of Health grant, which was awarded to provide solutions during this worldwide pandemic.