This Phone Scanner Can Detect Strokes Quickly. A Look at the Great Invention That Could Revolutionize Early Detection

Researchers hope to aid in early detection with the use of phone cameras.

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Picture this: a facial scanner for phones that can aid doctors in detecting strokes in seconds. As it turns out, biomedical engineers at RMIT University in Australia, and the University of São Paulo in Brazil, have recently developed a software that can do just this, powered by artificial intelligence. The researchers say the new method is significantly more accurate and faster than current technologies.

The study was led by researcher Guilherme Camargo de Oliveira from RMIT University and the University of São Paulo, under the supervision of team leader Professor Dinesh Kumar.

“Early detection of strokes is critical, as prompt treatment can significantly enhance recovery outcomes, reduce the risk of long-term disability, and save lives.”

The main goal of this tool is to promptly identify if a patient is in the post-stroke phase in order to obtain help as quickly as possible. 

According to the researchers, the tool is 82% accurate in detecting strokes but is not meant to replace full diagnostic tests.

It achieves its goal through facial detection and artificial intelligence algorithms. The researchers stress that it's currently challenging to detect strokes, given that the signs can be very subtle. The tool analyzes patterns related to facial expressions, facial symmetry, and specific muscle movements called action units.

The Facial Action Coding System (FACS), developed in the 1970s, categorizes facial movements based on the contraction or relaxation of facial muscles, providing a detailed framework for analyzing facial expressions.

“One of the key parameters that affects people with stroke is that their facial muscles typically become unilateral, so one side of the face behaves differently from the other side of the face.”

The researchers aim to create a mobile app that can detect these patterns and other neurological conditions that affect facial expressions. In fact, this will be key to enhancing the tool’s accuracy.

Image | RMIT University

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