Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Respir Med. 2011 Sep;105(9):1396-403. doi: 10.1016/j.rmed.2011.05.007. Epub 2011 Jun 14.
The standardized use of a stethoscope for chest auscultation in clinical research is limited by its inherent inter-listener variability. Electronic auscultation and automated classification of recorded lung sounds may help prevent some of these shortcomings.
We sought to perform a systematic review and meta-analysis of studies implementing computerized lung sound analysis (CLSA) to aid in the detection of abnormal lung sounds for specific respiratory disorders.
We searched for articles on CLSA in MEDLINE, EMBASE, Cochrane Library and ISI Web of Knowledge through July 31, 2010. Following qualitative review, we conducted a meta-analysis to estimate the sensitivity and specificity of CLSA for the detection of abnormal lung sounds.
Of 208 articles identified, we selected eight studies for review. Most studies employed either electret microphones or piezoelectric sensors for auscultation, and Fourier Transform and Neural Network algorithms for analysis and automated classification of lung sounds. Overall sensitivity for the detection of wheezes or crackles using CLSA was 80% (95% CI 72-86%) and specificity was 85% (95% CI 78-91%).
While quality data on CLSA are relatively limited, analysis of existing information suggests that CLSA can provide a relatively high specificity for detecting abnormal lung sounds such as crackles and wheezes. Further research and product development could promote the value of CLSA in research studies or its diagnostic utility in clinical settings.
由于听诊器在临床研究中的使用存在固有听众间变异性，因此其标准化使用受到限制。电子听诊和记录的肺部声音的自动分类可能有助于避免其中的一些缺陷。
我们旨在对实施计算机肺部声音分析（CLSA）以帮助检测特定呼吸疾病异常肺部声音的研究进行系统评价和荟萃分析。
我们通过 MEDLINE、EMBASE、Cochrane 图书馆和 ISI Web of Knowledge 搜索截至 2010 年 7 月 31 日的 CLSA 文章。经过定性评价，我们进行荟萃分析以估计 CLSA 检测异常肺部声音的敏感性和特异性。
在 208 篇文章中，我们选择了 8 篇进行综述。大多数研究使用驻极体麦克风或压电传感器进行听诊，并使用傅立叶变换和神经网络算法进行肺部声音的分析和自动分类。使用 CLSA 检测喘鸣或爆裂音的总体敏感性为 80%（95%CI 72-86%），特异性为 85%（95%CI 78-91%）。
尽管关于 CLSA 的高质量数据相对有限，但对现有信息的分析表明，CLSA 可以为检测爆裂音和喘鸣等异常肺部声音提供相对较高的特异性。进一步的研究和产品开发可以提高 CLSA 在研究中的价值，或在临床环境中的诊断效用。
