数字语音分析作为壮壮的生物标志物
Konstantina Vouzouneraki1, Fredrik Nylén2, Jenny Holmberg2
1Department of Public Health and Clinical Medicine, Umeå University, SE-901 87 Umeå, Sweden.
The Journal of clinical endocrinology and metabolism
|October 4, 2024
概括
数字语音分析使用机器学习准确地识别了壮病患者. 这种方法在检测疾病方面比专家内分泌学家更有效,这种疾病通常会导致语音障碍.
科学领域:
- 内分泌学 在内分泌学.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 巨症的诊断往往会延迟,导致患者的发病率增加.
- 由于口腔面部和喉的变化导致的声音变化是壮病患者的常见症状.
研究的目的:
- 调查数字语音分析作为巨的诊断生物标志物的实用性.
- 通过语音模式利用广泛的声学分析和机器学习来检测巨.
主要方法:
- 采集了151名壮病患者和139名使用手机的对照组的语音录音.
- 利用机器学习模型从持续的母音[a]录音中训练了3274个声学参数.
- 机器学习模型的性能 (ROC AUC 0.84) 与12位内分泌学家 (ROC AUC 0.69) 的性能进行了比较.
主要成果:
- 机器学习模型在识别壮病患者方面取得了很高的准确性.
- 壮症患者报告的语音问题显著增加 (VHI 6 vs 2,P < .01) 和临床显著语音障碍的更高患病率 (22.5% vs 3.6%).
结论:
- 数字语音分析提供了一种非常准确的,非侵入性的方法,用于从简短的语音样本中检测巨.
- 与健康个体相比,壮症患者的声音障碍负担更大.
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