ICDS News

RISE engineers support cardiology collaboration

Editor’s Note: A version of this article was originally published on Penn State News.

Penn State Institute for Computational and Data Sciences (ICDS) RISE engineers, with support from the Penn State Clinical and Translational Science Institute (CTSI)’s see grant program, were able to collaborate with University researchers and a team of physicians from the Penn State Heart and Vascular Institute on a project utilizing machine learning and existing electrocardiogram (ECG) data to help make more accurate predictions. The project, which has been ongoing since November, aims to develop novel algorithms for point-of-care, in-house use and for technology licensing.

Daniel Asante Otchere, ICDS research computing software engineer (RISE), under the supervision of Justin Petucci, ICDS R&D (RISE) engineer, is leading the artificial intelligence and computational support of the project alongside Ankit Maheshwari, assistant professor of medicine at Penn State and project lead researcher.

In September, an initial pilot study was published in the journal Heart Rhythm. The publication reported a model that could predict whether a patient with stroke of unknown cause would develop atrial fibrillation (AFib) by analyzing one heartbeat from a standard ECG test.

The research team wanted to see if they could use this standard test to predict AFib instead of relying on a loop recorder. The current standard of care is to implant a loop recorder, according to Penn State News. A loop recorder is a device that tracks heart activity to check for AFib and is placed under the skin. The team compiled a small data set using existing ECG data from Penn State comprising patients with cryptogenic stroke, or stroke without a clear cause, who had these loop recorders implanted. They also took data from 12-lead ECGs. Using machine learning algorithms, the team developed a model that could take the data and predict whether they would, or would not, develop AFib. The model correctly classified 80% of patients in the test cohort, Penn State News said.

Researchers are also aiming to expand the database, which would allow for broader applications according to Maheshwari.  The multi-year project has the potential to have a broader impact by implementing predictive modeling in other heart-related areas, for example, predicting when to use pacemakers in patients undergoing transcatheter aortic valve replacement procedures.

“This could lead to better patient selection and outcomes, helping doctors identify who is most likely to benefit from the procedure and who might face complications,” Maheshwari said. “These advancements highlight the great potential of machine learning to make heart care more efficient.”

The team is also focused on validating their predictive models. The goal is to have a fully functional database enabling them to conduct larger-scale studies, as well as to potentially apply for additional funding to support randomized, controlled trials. The initial funding was awarded through CTSI’s seed grant program for 2024-2025, alongside various other projects using machine learning. The goal of CTSI’s program is to establish collaborations needed to realize the potential of AI in biomedical and health research. CTSI’s Informatics Core, the National Center for Advancing Translational Sciences and the National Institutes of Health, through grant UL1 TR002014, are also supporting this research.

“Our efforts not only hold promise for improving patient care but also represent a paradigm shift in how cardiovascular diseases may be diagnosed and managed in the coming years,” Maheshwari said.

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