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通过人工智能识别患有下呼吸道感染的老年患者 分析咳模式声音
Yuling Li1, Siyu Ye2, Zaiting Ye3
1Respiratory Department, The Sixth Affiliated Hospital of Wenzhou Medical University, 323000 Lishui, Zhejiang, China.
自动咳声分析显示,在老年患者中,有可能诊断下呼吸道感染 (LRTI). 虽然在本研究中没有明确诊断,但咳特征与LRTI相关,表明未来的诊断价值.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 呼吸系统药物 呼吸系统药物
- 声学分析 声学分析
背景情况:
- 自动咳声音识别是诊断呼吸系统疾病的一个有前途的工具.
- 这项研究专门评估了咳声音在患有下呼吸道感染 (LRTI) 的老年人中的诊断效用.
研究的目的:
- 在怀疑患有LRTI的老年患者中调查咳声音特征的诊断价值.
- 为了比较感染和未感染的老年人之间的咳声音特征.
主要方法:
- 83名怀疑患有LRTI的老年患者被纳入.
- 咳的声音被记录下来,咳时间 (T1,T0),声音频率和分贝全尺度 (dBFs) 等特征被使用Mel Frequency Cepstrum系数提取出来.
- 用接收器操作特征 (ROC) 曲线分析来评估诊断疗效.
主要成果:
- 与未感染组相比,被诊断患有LRTI的患者表现出更高的咳声音指数,尽管统计学意义有所不同.
- 个别指数的曲线下面积 (AUC) 值在0.486到0.680之间,综合指数AUC为0.696.
- 该研究包括22名感染LRTI的老年患者和13名未感染的老年患者.
结论:
- 咳的声音表明与老年人群中LRTI的相关性.
- 尽管观察到相关性,但研究的小样本大小阻止了关于自动咳识别显而易见的诊断价值的最终结论.
- 在老年LRTI患者中,自动咳分析的诊断潜力仍然是未来研究的一个有希望的领域.
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