AI-Based Smartphone App Identifies Stroke Symptoms: A Cutting-Edge Approach

AI-Based Smartphone App Identifies Stroke Symptoms: A Cutting-Edge Approach

Introduction:

The use of AI in smartphone technology has taken a significant leap forward with the development of a groundbreaking app that can detect stroke symptoms. Combining machine learning and artificial intelligence, researchers at the UCLA David Geffen School of Medicine and medical facilities in Bulgaria have created a smartphone app that can accurately identify physical signs of strokes. By capturing videos of stroke patients and analyzing facial asymmetry, arm weakness, and speech abnormalities, the app can detect strokes with impressive accuracy. This exciting discovery has the potential to revolutionize stroke care, enabling early detection and intervention, and improving the lives of stroke sufferers.

Full Article: AI-Based Smartphone App Identifies Stroke Symptoms: A Cutting-Edge Approach

Smartphone App Uses AI to Detect Stroke Symptoms

An innovative smartphone app that uses machine learning and AI to precisely identify the physical symptoms of strokes was described by researchers at the 20th Annual Meeting of the Society of Neurointerventional Surgery.

Introduction

Researchers from the UCLA David Geffen School of Medicine and many medical facilities in Bulgaria participated in the study titled “Smartphone-Enabled AI Algorithms for Autonomous Stroke Detection.” They gathered information from 240 stroke patients at four large stroke centers. The researchers used cell phones to capture videos of the patients and evaluate their arm strength within 72 hours of their symptoms arising to spot evidence of facial asymmetry, arm weakness, and speech abnormalities, which are frequently linked to strokes.

Examining Facial Asymmetry

The researchers used Artificial Intelligence techniques to examine 68 facial landmark sites to examine facial asymmetry. This involved analyzing videos captured by the smartphone app to detect any asymmetrical features in a person’s face. Facial asymmetry is a common symptom of strokes and by detecting it early, medical intervention can be initiated promptly.

Evaluating Arm Weakness

Arm weakness was evaluated using a smartphone’s built-in 3D accelerometer, gyroscope, and magnetometer. These sensors were used to measure the strength and fluidity of arm movements, helping to identify any abnormalities indicative of a stroke. By capturing data related to arm weakness, the app contributes to the overall assessment of stroke symptoms.

Assessing Speech Abnormalities

Speech modifications were evaluated using mel-frequency cepstral coefficients, which translate sound waves into pictures. By contrasting normal versus slurred speech patterns, the app can detect any speech abnormalities associated with strokes. Early identification of speech abnormalities is crucial for timely stroke diagnosis and treatment.

Promising Early Detection

This smartphone app has great promise for the early detection of stroke symptoms, enabling immediate medical intervention and enhancing survival and recovery prospects. Dr. Radoslav Raychev, a vascular and interventional neurologist from UCLA’s David Geffen School of Medicine, showed enthusiasm for the app and using machine learning to treat stroke. The software is intended to revolutionize stroke care and enhance the lives of stroke sufferers.

Summary: AI-Based Smartphone App Identifies Stroke Symptoms: A Cutting-Edge Approach

An innovative smartphone app that uses machine learning and AI to detect the physical symptoms of strokes has been developed by researchers at the UCLA David Geffen School of Medicine and medical facilities in Bulgaria. The app analyzes videos captured on cell phones to identify facial asymmetry, arm weakness, and speech abnormalities, which are common signs of stroke. By using artificial intelligence techniques, the app can accurately identify strokes in almost all cases. This breakthrough has the potential to greatly improve the early detection of stroke symptoms, leading to immediate medical intervention and better outcomes for stroke patients.

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