Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis.
Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis.
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DOI：
10.1016/j.rmed.2011.05.007
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发表时间：
2011-09
影响因子：
4.3
通讯作者：
Check, William
中科院分区：
文献类型：
--
作者：
Gurung, Arati;Scrafford, Carolyn G.;Tielsch, James M.;Levine, Orin S.;Check, William
在临床研究中，听诊器用于胸部听诊的标准化使用受到其固有的听者间差异的限制。电子听诊和记录肺音的自动分类可能有助于防止这些缺点。我们试图对实施计算机肺音分析（CLSA）的研究进行系统回顾和荟萃分析，以帮助检测特定呼吸系统疾病的异常肺音。我们检索MEDLINE、EMBASE、科克伦图书馆和ISI Web of Knowledge中关于CLSA的文章，截止日期为2010年7月31日。在定性回顾之后，我们进行了一项荟萃分析，以评估CLSA检测异常肺音的敏感性和特异性。在识别的208篇文章中，我们选择了8项研究进行综述。大多数研究采用驻极体麦克风或压电传感器进行听诊，并采用傅立叶变换和神经网络算法进行肺音分析和自动分类。使用CLSA检测哮鸣音或爆裂音的总体灵敏度为80%（95%CI 72-86%），特异性为85%（95%CI 78-91%）。虽然CLSA的质量数据相对有限，但对现有信息的分析表明，CLSA可以提供相对较高的特异性来检测异常肺音，如爆裂音和喘息。进一步的研究和产品开发可以提高CLSA在研究中的价值或其在临床环境中的诊断实用性。
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 these shortcomings. We sought to perform a systematic review and meta-analysis of studies implementing computerized lung sounds 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 setting.
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DOI：
10.1590/s0100-879x2009000700013
发表时间：
2009-07-01
影响因子：
2.3
作者：
Riella, R.J.;Nohama, P.;Maia, J.M.
通讯作者：
Maia, J.M.
影响因子：
7.7
作者：
Kandaswamy, A;Kumar, CS;Malmurugan, N
通讯作者：
Malmurugan, N
DOI：
10.1006/cbmr.1999.1522
发表时间：
1999-10-01
期刊：
COMPUTERS AND BIOMEDICAL RESEARCH
影响因子：
--
作者：
Rietveld, S;Oud, M;Dooijes, EH
通讯作者：
Dooijes, EH
影响因子：
8
作者：
Andres, Emmanuel;Reichert, Sandra;Brandt, Christian
通讯作者：
Brandt, Christian
DOI：
10.1164/ajrccm.162.3.9905104
发表时间：
2000-09-01
影响因子：
24.7
作者：
Gross, V;Dittmar, A;von Wichert, P
通讯作者：
von Wichert, P