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声音作为儿科健康的生物标志物:一个范围广泛的审查
Hannah Paige Rogers1, Anne Hseu2, Jung Kim3
1Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
Children (Basel, Switzerland)
|June 27, 2024
概括
通过人工智能 (AI) 分析儿科语音显示出对早期发现健康状况的前景. 人工智能模型和语音分析方法的标准化对于儿童的临床应用至关重要.
科学领域:
- 生物标志物和计算医学
- 儿科健康信息学 儿科健康信息学
背景情况:
- 人的声音是儿童健康的潜在的非侵入性生物标志物.
- 人工智能 (AI) 为语音数据提供了先进的分析能力.
- 早期发现和监测儿科病情可以显著改善结果.
研究的目的:
- 综合当前关于人工智能应用的知识,分析儿科语音作为健康生物标志物.
- 审查使用儿童 (0-17岁) 语音记录进行健康状况分析的研究.
主要方法:
- 对62项涉及儿科语音录音和人工智能分析的研究进行了范围审查.
- 提取关于研究特征,参与者人口统计,记录来源,特征提取和AI模型的数据.
- 对39个人工智能模型的准确性,灵敏性和特异性的评估.
主要成果:
- 研究重点关注发育,呼吸,言语和语言疾病,特别是自闭症谱系障碍,智力障碍,窒息和喘.
- 最常见的特征提取和人工智能方法是Mel-Frequency Cepstral Coefficients (MFCCs) 和支持向量机 (SVMs). 它们是最常见的特征提取和人工智能方法.
- 人工智能对儿科语音的分析显示了作为一种具有成本效益的诊断工具的潜力.
结论:
- 人工智能驱动的儿科语音分析是各种健康状况的有希望的生物标志物.
- 特征提取和AI模型的标准化对于临床翻译至关重要.
- 进一步的研究可以为全球儿科患者群体带来创新的诊断工具.
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