Machine learning identifies physical signs of stroke

Machine learning detects physical signs of stroke

The study involved 240 stroke patients from four metropolitan stroke centers. 

Within 72 hours of the onset of symptoms, the researchers recorded videos of the patients. They tested their arm strength to identify facial asymmetry, arm weakness, and speech changes—well-known physical signs of stroke.

Utilising machine learning to optimise health

To evaluate facial asymmetry, the researchers employed machine learning techniques to analyse 68 facial landmark points. They utilised a smartphone’s built-in 3D accelerometer, gyroscope, and magnetometer data to test arm weakness. 

Mel-frequency cepstral coefficients were employed to detect speech changes, converting sound waves into images to compare standard and slurred speech patterns.

The app was then evaluated using neurologists’ reports and brain scan data, demonstrating high sensitivity and specificity in diagnosing stroke accurately in nearly all cases.

Revolutionising stroke care with early detection

Dr Radoslav Raychev, a vascular and interventional neurologist from UCLA’s David Geffen School of Medicine, expressed excitement about the potential impact of this app and machine learning technology on stroke care.

Identifying stroke symptoms swiftly and accurately is critical to ensure patient survival and facilitate regaining independence. With this app’s deployment, the researchers hope to transform lives and improve the landscape of stroke care.

The revolutionary stroke detection app utilising machine learning shows promise in aiding the early identification of stroke symptoms, potentially saving lives and improving care.

This innovative application can play a pivotal role in transforming stroke care outcomes. Early detection is paramount in the treatment of strokes, as it allows for timely intervention and medical attention, which can make the difference between life and death for affected individuals.