Smartphone Image
The test is powered by a machine learning algorithm that could significantly reduce the amount of time it takes physicians to diagnose a stroke. (Credit: Houston Methodist Hospital)

A new tool could diagnose a stroke based on abnormalities in a patient’s speech ability and facial muscular movements, and with the accuracy of an emergency room physician — all within minutes from an interaction with a smartphone.

Researchers have developed a machine learning model to aid in, and potentially speed up, the diagnostic process by physicians in a clinical setting. The team’s novel approach is the first to analyze the presence of stroke among actual emergency room patients with suspicion of stroke by using computational facial motion analysis and natural language processing to identify abnormalities in a patient’s face or voice, such as a drooping cheek or slurred speech.

To train the computer model, the researchers built a dataset from more than 80 patients experiencing stroke symptoms at Houston Methodist Hospital in Texas. Each patient was asked to perform a speech test to analyze their speech and cognitive communication while being recorded on an Apple iPhone.

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