Biomedical engineers at RMIT University have unveiled a new smartphone tool equipped with AI capabilities that could revolutionize the early detection of strokes.
This new technology uses AI to study facial expressions. It helps paramedics spot stroke symptoms quickly, which could save lives and prevent long-term disability.
Strokes happen when blood flow to the brain is blocked, which starves brain cells of oxygen and nutrients. Strokes are a major cause of disability and death worldwide. Notably, the time to act during a stroke is quite short. Every minute becomes crucial and can significantly impact a patient’s recovery.
“Early detection of stroke is critical, as prompt treatment can significantly enhance recovery outcomes, reduce the risk of long-term disability, and save lives,” emphasized Professor Dinesh Kumar from RMIT’s School of Engineering, who led the research.
AI face-screening tech: Analyzing Facial Expressions for Stroke Symptoms
Under the guidance of Professor Kumar, PhD scholar Guilherme Camargo de Oliveira spearheaded the development of this groundbreaking technology. The software utilizes AI algorithms to analyze facial symmetry and muscle movements associated with stroke.
While not intended to replace comprehensive clinical assessments, the smartphone tool boasts an impressive 82% accuracy rate in identifying stroke symptoms.
“Our face-screening tool complements existing diagnostic methods by offering a rapid initial assessment,” explained Kumar.
The technology operates by employing facial expression recognition, a method that evaluates asymmetry and changes in facial muscle actions known as action units. Leveraging the Facial Action Coding System (FACS), originally devised in the 1970s, the tool meticulously categorizes and analyzes facial movements indicative of stroke.
“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,” explained de Oliveira.
The study involved video recordings of facial expression examinations from individuals post-stroke and healthy controls. This data was crucial for refining and validating the effectiveness of the tool.
Expanding applications: Future directions for stroke detection tool
Recognizing stroke symptoms can be challenging, particularly in diverse patient populations where symptoms may present differently or be overlooked altogether.
“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,” Kumar pointed out.
Moreover, subtle signs of stroke can be missed, especially when attending to patients of different racial or gender backgrounds, Kumar added.
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The team aims to transform the prototype into a user-friendly smartphone application.
They are working with healthcare providers to include the tool in current emergency response procedures. This will help first responders identify strokes and other neurological conditions that affect facial expressions more effectively.
“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,” remarked Kumar on future plans.
The study was published in Computer Methods and Programs in Biomedicine.
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Sujita Sinha A versatile writer, Sujita has worked with Mashable Middle East and News Daily 24. When she isn't writing, you can find her glued to the latest web series and movies.
