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Digital & Innovation

Australian AI-powered tool promises rapid stroke detection

Health Industry Hub | June 20, 2024 |

Digital & Innovation: An Australian innovation promises to transform stroke care by enabling quicker intervention and improving patient outcomes through advanced, user-friendly technology.

A smartphone tool designed by biomedical engineers at RMIT University aims to improve stroke detection by allowing paramedics to identify the condition within seconds, significantly faster and more accurately than current methods.

Strokes, which affect millions globally, occur when blood flow to the brain is disrupted, leading to potential brain damage within minutes. Prompt diagnosis is crucial to prevent long-term disability and save lives.

Led by PhD scholar Guilherme Camargo de Oliveira and supervised by Professor Dinesh Kumar from RMIT School of Engineering, the team developed AI capabilities behind the software.

“We have developed a simple smartphone tool that paramedics can use to instantly determine whether a patient is post-stroke and inform the hospital before leaving the scene,” Professor Kumar said.

The tool, boasting an 82% accuracy rate with the best sensitivity of 91%, complements but does not replace comprehensive clinical tests, aiming to quickly identify patients needing urgent treatment.

“Studies indicate that nearly 13% of strokes are missed in emergency departments and at community hospitals, while 65% of patients without a documented neurological examination experience undiagnosed stroke,” explained Professor Kumar.

“Given that many strokes occur at home and initial care is often provided by first responders in non-ideal conditions, there is an urgent need for real-time, user-friendly diagnostic tools.”

The AI-driven technology uses facial expression recognition to analyse symmetry and muscle movements, employing the Facial Action Coding System (FACS) to categorise facial actions.

“One of the key parameters that affects people with stroke is that their facial muscles typically become unilateral,” de Oliveira said. “We’ve got the AI tools and the image processing tools that can detect whether there is any change in the asymmetry of the smile – that is the key to detection in our case.”

“We want to be as sensitive and specific as possible. We are now working towards an AI tool with additional data and where we are going to be considering other diseases as well,” Professor Kumar stated.

“Collaboration with healthcare providers will be crucial to integrate this App into existing emergency response protocols, providing paramedics with an effective means of early stroke detection.”

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