Two Illinois State University professors report major progress in the first year of a new artificial intelligence lab on campus researching early detection for atrial fibrillation [AF].
AF is considered the most common type of cardiac arrhythmia, or irregular heartbeat, and is considered the leading cardiac cause of stroke. It increases the risk of stroke by five times and can lead to significant health deterioration and death. It affects one in 22 Americans, according to a 2024 report from National Heart, Lung and Blood Institute.
"If you could predict that earlier, then it could have a tremendous impact on the overall outcome of many individuals," said Mennonite College of Nursing professor Marilyn Prasun, adding the goal is to help close the gap in care for people in underserved rural communities in Central Illinois.
Prasun is leading the project with ISU Information Technology assistant professor Nariman Ammar. They are using machine learning and artificial intelligence with clinical experience to more accurately detect AF in high-risk patients.
“Actually, we have been very successful so far,” Prasun said. “We have examined several of the variables and have refined a list of those variables and are moving forward with developing some algorithms that we ultimately can evaluate."
The project is also funded by a training grant from National Institutes of Health’s AIM-AHEAD Program for Artificial Intelligence Readiness, or PAIR, seed program.
The project initially aimed to detect people that could go into atrial fibrillation within one to two years, but now the team is aiming to make that six months.
"If we're successful in that endeavor, we potentially could reduce negative outcomes," Prasun said.
Two graduate students and two undergraduate students are assisting in the study. Recent findings from the students on the team have also accelerated the project’s progress.
“The student group presented some early findings with regards to what they were identifying of those living with atrial fibrillation,” Prasun said. “Since that time [we have] completed a systematic literature review that we're working on moving forward for a publication."
Prasun’s area of expertise is knowledge in cardiovascular disease symptom management, atrial fibrillation and heart failure. Ammar specializes in machine learning and AI. Together, they make interpretations based on variables found by Prasun and applications then made by Ammar.
“The combination of the two of us demonstrates the beauty of how different disciplines can join together and ultimately lead to improved outcomes in a healthcare setting,” Prasun said.
The research team has also received data from MedStar’s research center that they gained access to from the grant. The variables and data they have received from MedStar could aid in identifying the risk for individuals with atrial fibrillation.
The grant also allowed the team to attend weekly meetings with representatives with similar interests in the field.
“When you're having meetings like that, it really facilitates your awareness and learning of next steps,” Prasun said.
