深度学习方法用于通过基于音节的语音分析检测失调,使用日常对话来检测失调
Seokhyeon Heo1, Kyeong Eun Uhm1, Doyoung Yuk1
1Department of Rehabilitation Medicine, Konkuk University Medical Center, 120-1 Neungdong-ro, Gwangjin-gu, Seoul, 05030, Republic of Korea.
Scientific reports
|August 31, 2024
概括
一个新的深度学习模型准确地检测了老年人常见的吞障碍 - - 吞障碍. 这种非侵入性方法分析语音模式,用于早期诊断,改善患者的治疗结果.
科学领域:
- 老年学是一门学科.
- 语音语言病理学 语言病理学
- 医疗保健中的人工智能
背景情况:
- 吞障碍 (吞困难) 在老年人中很普遍,增加了严重健康问题的风险.
- 早期发现食障碍对于及时干预和管理至关重要.
- 现有的诊断方法可能是侵入性的或不适合日常环境.
研究的目的:
- 评估一种新的深度学习模型,用于使用音节分割的语音数据来诊断失调症.
- 评估模型在区分患有失消症和没有失消症的个体方面的有效性.
- 探索人工智能在日常生活中用于非侵入性,早期食障碍检测的潜力.
主要方法:
- 在日常对话中收集了来自16名消化不良患者和24名对照者的音频数据.
- 利用语音到文本模型将音频细分成音节.
- 应用了卷积神经网络用于二进制分类以识别缺食症.
- 使用视频光学吞研究结果验证了模型.
主要成果:
- 拼音细分分析实现了0.794的诊断准确度,0.901的灵敏度和0.687.7的特异性.
- 在个人层面,该模型显示整体准确率为0.900和AUC为0.953.
- 深度学习模型在区分失调患者与对照患者方面表现出很高的表现.
结论:
- 对音节分割语音的深度学习分析是早期检测失语症的一个有希望的工具.
- 开发的模型提供了一种非侵入性的,简单的和潜在的成本效益高的查功能障碍症的方法.
- 这种人工智能驱动的方法可以在日常环境中方便诊断失调症,改善老年护理.
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