使用人工智能对心脏,肺部和肠道声音进行自动分类和声学听觉
Yen-Sheng Lin1,2, Ansh Kapadia3, Eric B Ortigoza4
1Department of Orthopaedic Surgery, UT Southwestern Medical Center, Dallas, TX.
Research square
|August 6, 2025
概括
这项研究引入了人工智能 (AI) 框架,以准确分类心脏,肺部和肠道声音,提高诊断一致性. 人工智能模型表现出强的表现,为临床听觉提供客观支持.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 临床诊断 临床诊断 临床诊断
背景情况:
- 听觉是一种基本的临床技能,用于通过心脏,肺和肠道声音来诊断疾病.
- 诊断的准确性严重依赖于临床医生的专业知识,导致变化.
- 技术进步需要客观,可重复的诊断工具.
研究的目的:
- 开发一种新型的人工智能 (AI) 框架,用于自动分类和生理声音的声学差异化.
- 解决客观和可重复的诊断支持在临床听觉方面的需求.
- 探索人工智能辅助听觉,以改善差异诊断.
主要方法:
- 利用监督机器学习和信号处理技术.
- 从数字化的心脏,肺部和肠道声音中提取了声学特征.
- 使用六种AI模型,分析了来自不同无症状人群的光谱,时间和形态特征.
主要成果:
- 取得了优异的预测准确度,从65.00%到91.67%不等.
- 证明了强大的验证准确度,在83.87%至94.62%之间.
- 成功分类和声学区分的多器官声音.
结论:
- 人工智能框架提供了一个有前途的方法,用于客观的诊断支持临床听觉.
- 潜在的应用包括医疗教育,远程医疗和持续的患者监测.
- 这项工作推进了人工智能辅助听觉,旨在提高诊断准确性和标准化.
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Gallops:
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