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Related Concept Videos

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Related Experiment Video

Updated: Jul 24, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Artificial Intelligence-Based Speech Analysis System for Medical Support.

Eui-Sun Kim1, Dong Jin Shin2, Sung Tae Cho3

  • 1Department of Media, Soongsil University, Seoul, Korea.

International Neurourology Journal
|July 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered voice analysis system for early detection of neurogenic bladder in stroke patients. The system achieves high accuracy in identifying voice abnormalities, aiding in timely intervention and prevention.

Keywords:
Deep learningDiagnosis support systemNeurogenic bladderSpeech recognitionStroke

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Speech Analysis

Background:

  • Stroke can significantly impact neurogenic bladder symptoms and presentation.
  • Abnormal linguistic and facial characteristics are observed in stroke patients with neurogenic bladder.
  • Early detection and prevention are crucial for managing stroke-related neurogenic bladder.

Purpose of the Study:

  • To develop an AI-based speech analysis platform for accurate voice analysis in stroke patients with neurogenic bladder.
  • To enable early detection and prevention of neurogenic bladder sequelae in stroke survivors.

Main Methods:

  • An artificial intelligence-based speech analysis diagnostic system was developed.
  • Voice recordings of stroke patients speaking a specific sentence were analyzed for unique feature data.
  • A mobile application was used to provide a voice alarm service, classifying abnormalities and issuing alerts.

Main Results:

  • The AI system achieved high test accuracy: 98.7% for normal data and 99.6% for abnormal data.
  • The model was validated using training and testing data, processing 30 abnormal and 30 normal data points in real time.
  • The system demonstrated robust performance in classifying voice abnormalities associated with neurogenic bladder in stroke patients.

Conclusions:

  • AI-driven digital treatments are essential for managing chronic conditions like stroke that lead to significant long-term impairments.
  • The developed AI healthcare device offers timely and safe mobile medical services for stroke patients.
  • This approach aims to reduce national social costs by improving patient care and preventing severe consequences of stroke-related neurogenic bladder.