When having a stroke, a quick diagnosis is of the essence. Now, a new tool created by researchers at Penn State and Houston Methodist Hospital could diagnose a stroke within minutes simply through the use of a smartphone.
“When a patient experiences symptoms of a stroke, every minute counts,” said James Wang, professor of information sciences and technology at Penn State. “But when it comes to diagnosing a stroke, emergency room physicians have limited options: send the patient for often expensive and time-consuming radioactivity-based scans or call a neurologist — a specialist who may not be immediately available — to perform clinical diagnostic tests.”
The new tool bases its analysis on a patient’s speech ability and facial muscular movements and provides results with the accuracy of an emergency room physician. The novel technology uses a machine learning model to achieve this impressive task.
“Currently, physicians have to use their past training and experience to determine at what stage a patient should be sent for a CT scan,” said Wang. “We are trying to simulate or emulate this process by using our machine learning approach.”
The tool could be used by emergency room physicians to more quickly determine critical next steps for the patient or by caregivers or patients themselves to make self-assessments before even reaching the hospital. Both these approaches would give the patient a key advantage in surviving a stroke.
Furthermore, the novel test was found to perform with 79% accuracy (comparable to clinical diagnostics by emergency room doctors) and was reported to have the ability to assess a patient in as little as four minutes.
"The earlier you can identify a stroke, the better options (we have) for the patients,” added Stephen T.C. Wong, John S. Dunn, Sr. Presidential Distinguished Chair in Biomedical Engineering at the Ting Tsung and Wei Fong Chao Center for BRAIN and Houston Methodist Cancer Center. “That’s what makes an early diagnosis essential.”