一个基于语音的算法可以预测美国成年人的2型糖尿病状况:来自Colive Voice研究的发现
Abir Elbéji1, Mégane Pizzimenti1, Gloria Aguayo1
1Deep Digital Phenotyping Research Unit. Department of Precision Health, Luxembourg Institute of Health, 1 A-B rue Thomas Edison, L-1445 Strassen, Luxembourg.
PLOS digital health
|December 19, 2024
概括
一个新的基于语音的算法显示了对查2型糖尿病 (T2D) 的希望. 这种非侵入性方法准确地预测T2D状态,提供一种可扩展的方法来减少全球未被诊断的病例.
科学领域:
- 生物医学工程 生物医学工程
- 计算健康 计算健康
- 内分泌学 在内分泌学.
背景情况:
- 未被诊断的2型糖尿病 (T2D) 构成了全球重大健康挑战.
- 目前的选方法可能缺乏可访问性和可扩展性.
- 早期检测需要创新的,非侵入性的方法.
研究的目的:
- 研究基于语音算法的有效性,用于预测成年人T2D状态.
- 开发一种可扩展且非侵入性的T2D查工具.
- 将语音算法性能与现有的风险评估得分进行比较.
主要方法:
- 从607名美国参与者的语音录音分析 (Colive Voice研究).
- 使用混合 BYOL-S/CvT 嵌入的性别特定算法的开发.
- 通过交叉验证 (准确性,特异性,敏感性,AUC) 和布兰德-阿尔特曼分析与ADA评分进行评估.
主要成果:
- 语音算法显示出良好的预测能力 (AUC为75%的男性,71%的女性).
- 准确预测T2D病例 (71%的男性,66%的女性).
- 老年女性 (AUC 74%) 和高血压患者 (AUC 75%) 的表现改善,与ADA评分达成>93%的一致.
结论:
- 基于语音的算法为T2D提供了一个潜在的可访问,具有成本效益和非侵入性的查工具.
- 有希望的结果需要进一步验证,特别是在早期T2D和多样化的群体中.
- 这项技术可以显著帮助减少全球未被诊断的2型糖尿病.
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