NIH Grant Supports Development of AI Tools To Identify High-Risk COVID-19 Patients

July 29, 2020

NIH Grant Supports Development of AI Tools To Identify High-Risk COVID-19 Patients

Rensselaer-led research team is fast-tracking research to improve screening and treatment

TROY, N.Y. — With COVID-19 still spreading in the United States, where it has already killed more than 140,000 people, improved screening and treatment options are critically important for high-risk patients with comorbidities, such as diabetes, pulmonary disease, and cardiovascular disease.

A new grant from the National Institutes of Health (NIH) will support the rapid development and integration of a series of artificial intelligence algorithms that will analyze multiple pieces of health data — from chest computed tomography (CT) images to vital signs — in order to help clinicians assess disease severity and predict patient outcomes. The effort is being led by Pingkun Yan, an assistant professor of biomedical engineering at Rensselaer Polytechnic Institute.

“Screening out the high-risk patients who may need intensive care later, and monitoring them more closely to provide early intervention, may help save their lives,” said Yan, who is a member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

According to Yan, the few artificial intelligence tools that are currently available in the COVID-19 fight can only help doctors determine the severity of lung infection brought about by the disease. They fail to assess and account for comorbidities.

The research team will create a framework of algorithms that can integrate critical information about a patient, including CT scans that assess the severity of lung infection, patient demographic information, vital signs, and laboratory blood test results. Yan’s team previously developed a series of AI algorithms that assess a patient’s risk of cardiovascular disease using chest CT scans. That work will be integrated into this new research.

“My group has been focusing on using artificial intelligence and deep learning to analyze medical imaging data with an emphasis on translating the technology from benchside to bedside,” Yan said. “This focus built a solid foundation for us, so when the crisis emerged, with our clinical collaborator at Massachusetts General Hospital, we quickly identified the clinical needs and started working on a solution.”

“Rensselaer expertise in artificial intelligence, as exhibited by Professor Yan’s work, makes rapid response to the COVID-19 pandemic possible,” said Deepak Vashishth, the director of CBIS. “New and improved tools, like the one targeted here to identify high-risk COVID-19 patients, are essential in this public health crisis.”

In partnership with Massachusetts General Hospital, this NIH-supported research will be fast-tracked, with the hope of implementing the team’s algorithms as quickly as possible.

“It is tremendously important to me and my team that we can contribute our knowledge and skills to fight the COVID-19 pandemic,” Yan said. “It is our way to answer ‘Why not change the world?’ — the unofficial Rensselaer motto.”


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