在使用长短期记忆神经网络的患者呼吸系统音频文件中检测和分类肺病
Pinzhi Zhang1, Alagappan Swaminathan2, Ahmed Abrar Uddin1
1College of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Frontiers in medicine
|November 29, 2023
概括
深度学习模型使用呼吸声准确诊断COPD和肺炎等肺部疾病. 长短期记忆 (LSTM) 算法实现了98.82%的准确性,提供实时诊断功能.
科学领域:
- 计算智能是一种计算智能.
- 医疗信息学医学信息学
- 呼吸系统药物 呼吸系统药物
背景情况:
- 精确诊断呼吸道疾病对于有效的患者管理至关重要.
- 现有的诊断方法可能是侵入性的或耗时的.
- 需要新的方法来提高诊断的准确性和效率.
研究的目的:
- 开发和评估一种用于精确诊断肺病的新方法,使用患者呼吸系统音频录音.
- 确定最有效的深度学习模型来分类呼吸系统疾病,包括慢性阻塞性肺病 (COPD),上呼吸道感染 (URTI),支气管病,肺炎和支气管炎.
主要方法:
- 训练了四种深度学习算法:卷积神经网络 (CNN),长期短期记忆 (LSTM),CNN-LSTM和CNN-BLSTM.
- 利用920名患者通过数字耳语镜 (呼吸声数据库) 记录的呼吸声音频文件的数据集.
- 使用准确度,精度,回忆和F1分数来评估模型性能.
主要成果:
- 长短期记忆 (LSTM) 算法表现出卓越的性能.
- LSTM的整体准确率为98.82%,F1得分为0.97.
- 该模型的有效性源于其捕捉音频数据中的顺序模式的能力.
结论:
- 深度学习,特别是LSTM,提供了一种非常准确和高效的方法来通过音频记录来诊断呼吸系统疾病.
- 这种方法可以实时预测肺部疾病,从而有可能改善诊断工作流程.
- 这些发现支持将人工智能驱动的音频分析整合到呼吸系统健康的临床实践中.
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