CT-Based AI Tool Offers Accurate Outcomes Predictions For COVID-19 Patients

November 29, 2020
Whitney J. Palmer

By incorporating non-imaging data, the algorithm can effectively pinpoint which patients will need ICU intervention.

As the need for faster, accurate COVID-19 diagnostic tools continues to grow, another group of researchers has announced a tool that can help clinicians treat patients based on their specific needs and history.

Specifically, a team from Rensselaer Polytechnic Institute, with support from a National Institutes of Health grant, has developed a new algorithm that combines CT images of the patient’s lungs with non-imaging data, such as demographics, vital signs, and laboratory test results, to help providers pinpoint which COVID-19-positive patients will need intervention in the intensive care unit.

The team published the details of their algorithm recently in Medical Image Analysis.

To determine the algorithm’s efficacy, the team, led by Pingkun Yan, Ph.D., associate professor of biomedical engineering, tested the tool on datasets collected from 295 patients from three hospitals, one each in the United States, Iran, and Italy, comparing the algorithm’s treatment predictions to the treatment each patient actually needed and received.

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Based on their analysis, they determined that adding non-imaging features to the algorithm significantly improves prediction performance, enabling the algorithm to reach an AUC up to 0.884 and sensitivity as high as 96.1 percent. Consequently, Yan said, it can be a valuable clinical decision support tool for the management of COVID-19-positive patients.

“As a practitioner of AI, I do believe in its power,” he said. “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.”

In collaboration with researchers from Massachusetts General Hospital, Yan’s team is working to integrate this algorithm with another he previously developed that can use CT scans to assess cardiovascular disease risk. Underlying conditions, such as heart disease play a considerable role in COVID-19 mortality, he said, so being able to quantify a patient’s heart condition can help determine how the team can factor it into the AI prediction.

Ultimately, Yan said, the goal is to translate the algorithm into a method that doctors at Massachusetts General can use in clinical assessments – both with COVID-19 and beyond.

“We actually are seeing that the impact could go well beyond COVID diseases. For example, patients with other lung diseases,” he said. “Assessing their heart disease condition, together with their lung condition, could better predict their mortality risk so that we can help them to manage their condition.”

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