Machine language tool helps diagnose strokes with the speed of a smartphone

Researchers at Penn State and Houston Medical hope a machine learning tool to detect slurred speech and drooping cheeks via a smartphone can be used by caregivers for a quick assessment of stroke before an ER visit. (Penn State)

Researchers have built a machine learning model to aid in the diagnosis of strokes using a smartphone to detect abnormalities in speech and face muscle movements.

The researchers at Penn State and Houston Methodist Hospital are trying to emulate what a physician does when confronting a possible stroke victim in a clinical setting and before deciding whether to administer a CT scan, according to an article in Artificial Intelligence Research.

“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 who may not be available to perform clinical diagnostic tests,” said James Wang, professor of information sciences and technology at Penn State.

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The researchers are using facial motion analysis and natural language processing to detect abnormalities such as a drooping cheek or slurred speech. Their hope is that the app can be used by caregivers or patients to make self-assessments before going to a hospital.

The researchers relied on a dataset from 80 patients having stroke symptoms at Houston Methodist. Their ultimate model achieved 79% accuracy.  One of its central values is that it saves time in assessing a stroke. 

Millions of neurons die every minute in a stroke, but many studies suggest that many patients with moderate symptoms face having a diagnosis delayed by hours, according to Houston Methodist vascular neurologist John Volpi.  “The earlier you can identify a stroke, the better options for patients,” added Stephen T.C. Wong at Houston Methodist.

RELATED: Penn State receives sensors to enhance student education

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