PT  - JOURNAL ARTICLE
AU  - Ellington, Laura E
AU  - Gilman, Robert H
AU  - Tielsch, James M
AU  - Steinhoff, Mark
AU  - Figueroa, Dante
AU  - Rodriguez, Shalim
AU  - Caffo, Brian
AU  - Tracey, Brian
AU  - Elhilali, Mounya
AU  - West, James
AU  - Checkley, William
TI  - Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study
AID  - 10.1136/bmjopen-2011-000506
DP  - 2012 Jan 01
TA  - BMJ Open
PG  - e000506
VI  - 2
IP  - 1
4099  - http://bmjopen.bmj.com/content/2/1/e000506.short
4100  - http://bmjopen.bmj.com/content/2/1/e000506.full
SO  - BMJ Open2012 Jan 01; 2
AB  - Introduction WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries.Methods This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children's hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis.Discussion This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia.