LUNAR:用于LUNG听力呼吸检测的周期感知时间序列分析框架
Jisoo Lee1, Sa-Yoon Park2, Ji Soo Park3
1Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
Computers in biology and medicine
|August 28, 2025
概括
一个新的深度学习模型,LUNG听觉呼吸探测器 (LUNAR),自动检测来自原始肺部声音的呼吸周期. 这种人工智能工具减少了对专家解释的依赖,提高了呼吸系统疾病的诊断一致性.
科学领域:
- 医疗技术
- 医疗保健中的人工智能
- 呼吸系统药物
背景情况:
- 精确的呼吸循环检测对于诊断喘和肺炎等呼吸系统疾病至关重要.
- 目前的方法往往依赖于专业的临床医生,
- 现有的呼吸声分析人工智能模型需要手动注释, 阻碍自动化过程.
研究的目的:
- 推出LUNG听觉呼吸探测器 (LUNAR),这是一个用于自动呼吸周期检测的深度学习框架.
- 解决自动化呼吸声分析中手动注释的瓶问题.
- 开发一个强大的临床肺声分析工具,减少对专家专业知识的依赖.
主要方法:
- 开发了一个周期性深度学习框架LUNAR,
- 集成了一个新的呼吸周期意识模块 (RPAM) 与卷积神经网络.
- 在HF_Lung_V1上接受培训,并通过ICBHI和SNUCH_Lung数据集进行验证.
主要成果:
- 对于AP: 0. 880和F1: 0. 894来说,LUNAR表现非常出色.
- RPAM模块在AP中提高了12.0%,在F1得分中提高了6.3%.
- 该模型使用原始时间序列数据保持了成人和儿科患者的稳定结果.
结论:
- 在临床肺声分析中,LUNAR为自动呼吸循环检测提供了强大的解决方案.
- 该框架减少了对专业口译的需求,提高了资源有限的医疗服务的可用性.
- 通过多机构数据和扩展到异常呼吸声检测的潜在应用.
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