Kim Eui-Sun, Shin Dong Jin, Cho Sung Tae, Chung Kyung Jin
Department of Media, Soongsil University, Seoul, Korea.
Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea.
Int Neurourol J. 2023 Jun;27(2):99-105. doi: 10.5213/inj.2346136.068. Epub 2023 Jun 30.
Prior research has indicated that stroke can influence the symptoms and presentation of neurogenic bladder, with various patterns emerging, including abnormal facial and linguistic characteristics. Language patterns, in particular, can be easily recognized. In this paper, we propose a platform that accurately analyzes the voices of stroke patients with neurogenic bladder, enabling early detection and prevention of the condition.
In this study, we developed an artificial intelligence-based speech analysis diagnostic system to assess the risk of stroke associated with neurogenic bladder disease in elderly individuals. The proposed method involves recording the voice of a stroke patient while they speak a specific sentence, analyzing it to extract unique feature data, and then offering a voice alarm service through a mobile application. The system processes and classifies abnormalities, and issues alarm events based on analyzed voice data.
In order to assess the performance of the software, we first obtained the validation accuracy and training accuracy from the training data. Subsequently, we applied the analysis model by inputting both abnormal and normal data and tested the outcomes. The analysis model was evaluated by processing 30 abnormal data points and 30 normal data points in real time. The results demonstrated a high test accuracy of 98.7% for normal data and 99.6% for abnormal data.
Patients with neurogenic bladder due to stroke experience long-term consequences, such as physical and cognitive impairments, even when they receive prompt medical attention and treatment. As chronic diseases become increasingly prevalent in our aging society, it is essential to investigate digital treatments for conditions like stroke that lead to significant sequelae. This artificial intelligence-based healthcare convergence medical device aims to provide patients with timely and safe medical care through mobile services, ultimately reducing national social costs.
先前的研究表明，中风会影响神经源性膀胱的症状和表现，出现多种模式，包括异常的面部和语言特征。特别是语言模式很容易识别。在本文中，我们提出了一个平台，能够准确分析患有神经源性膀胱的中风患者的声音，实现对该病症的早期检测和预防。
在本研究中，我们开发了一种基于人工智能的语音分析诊断系统，以评估老年个体中与神经源性膀胱疾病相关的中风风险。所提出的方法包括在中风患者说出特定句子时记录其声音，对其进行分析以提取独特的特征数据，然后通过移动应用程序提供语音警报服务。该系统对异常情况进行处理和分类，并根据分析后的语音数据发出警报事件。
为了评估该软件的性能，我们首先从训练数据中获得验证准确率和训练准确率。随后，我们输入异常和正常数据应用分析模型并测试结果。通过实时处理30个异常数据点和30个正常数据点对分析模型进行评估。结果表明，正常数据的测试准确率高达98.7%，异常数据的测试准确率为99.6%。
中风导致神经源性膀胱的患者即使得到及时的医疗关注和治疗，也会经历长期后果，如身体和认知障碍。随着慢性病在我们老龄化社会中日益普遍，研究针对像中风这种会导致重大后遗症的病症的数字治疗方法至关重要。这种基于人工智能的医疗融合医疗设备旨在通过移动服务为患者提供及时和安全的医疗护理，最终降低国家社会成本。
