UCI researchers study use of machine learning to improve stroke diagnosis, access to timely treatment

Research to validate the identification of large vessel occlusions using machine learning.

Orange, Calif. – Clinicians at the University of California, Irvine have launched a research study to examine how the use of artificial intelligence and machine learning can expedite the detection of stroke in diagnostic imaging, optimize clinical workflows for stroke care, and improve patient outcomes. Notably, the study will evaluate acute stroke triage and identify areas for improvement.

“The goal of the study goes beyond performance metrics of machine learning algorithms and tries to answer whether these tools benefit our patients,” said Jennifer Soun, MD, the study’s principal investigator and an assistant professor of radiological sciences in the UCI School of Medicine and a neuroradiologist for UCI Health, the university’s clinical enterprise.

Stroke is among the leading causes of death and disability in adults worldwide, killing about 140,000 Americans each year. Someone in the U.S. has a stroke every 40 seconds and the length of time from the onset of symptoms to treatment can affect the likelihood of survival or the severity of disability in stroke survivors.

Strokes are caused by blockages in blood vessels in the brain, and strokes due to large vessel occlusions (LVO) can be particularly life-threatening. “Prompt diagnosis of an LVO is crucial to initiate life-saving stroke therapies, and AI tools have the potential to aid in stroke triage through immediate LVO detection and notification,” said Soun.

The five-year study, Comparison of Artificial Intelligence Based Large-Vessel Occlusion Solutions, formally began in August 2021 and is sponsored by Canon Medical Systems USA. The study will evaluate AI-based tools and how they affect stroke triage, treatment times and clinical outcomes.

As a Joint Commission-accredited Comprehensive Stroke Center, UCI Medical Center maintains high standards for stroke care including treatment with tissue plasminogen activator (tPA) and thrombectomy.

UCI’s commitment to advancing artificial intelligence and machine learning in clinical care includes the work of the Center for Artificial Intelligence in Diagnostic Medicine, launched in 2018 by Soun’s colleagues, Peter Chang, MD, and Danial Chow, MD. More recently, UCI launched an Institute of Precision Health to improve health care through the use of data and machine learning.

Disclosures: Dr. Peter Chang, an advisor on the study and an assistant professor in radiological sciences at UCI School of Medicine, is co-founder, chief medical officer and has an equity interest in Avicenna.ai, a company that may potentially benefit from the research results, and also serves on the company’s executive board. Dr. Daniel Chow, an advisor on the study and an assistant professor in radiological sciences at UCI School of Medicine, also has an equity interest in Avicenna.ai.

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About the University of California, Irvine: Founded in 1965, UCI is the youngest member of the prestigious Association of American Universities and is ranked among the nation’s top 10 public universities by U.S. News & World Report. The campus has produced three Nobel laureates and is known for its academic achievement, premier research, innovation and anteater mascot. Led by Chancellor Howard Gillman, UCI has more than 36,000 students and offers 224 degree programs. It is located in one of the world’s safest and most economically vibrant communities, and is Orange County’s second-largest employer, contributing $7 billion annually to the local economy and $8 billion statewide. Learn more about the university at www.uci.edu ›