Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

• Published in: BMJ open Vol. 12; no. 2; p. e059630
• Main Authors: Ahmed, Salahuddin, Mitra, Dipak Kumar, Nair, Harish, Cunningham, Steven, Khan, Ahad Mahmud, Islam, ASMD Ashraful, McLane, Ian Mitra, Chowdhury, Nabidul Haque, Begum, Nazma, Shahidullah, Mohammod, Islam, Muhammad Shariful, Norrie, John, Campbell, Harry, Sheikh, Aziz, Baqui, Abdullah H, McCollum, Eric D
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 09-02-2022; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Protocol
• Subjects: Acoustics; Algorithms; Antibiotics; Artificial intelligence; Auscultation; Automation; Bangladesh; Child, Preschool; Children & youth; Childrens health; Clinical Protocols; community child health; Cross-Sectional Studies; Fatalities; Humans; Hypotheses; Infant; Listening; Machine learning; Medical diagnosis; Medical personnel; Mortality; Noise; paediatric infectious disease & immunisation; Paediatrics; Pediatrics; Pneumonia; Pneumonia - diagnosis; Reproducibility of Results; respiratory infections; Respiratory Sounds - diagnosis; Sound; Bangladesh; respiratory infections; paediatric infectious disease & immunisation; community child health
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2021-059630

IntroductionThe WHO’s Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations.Methods and analysisIn a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel’s final interpretation as gold standard.Ethics and disseminationThe study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.Trial registration numberNCT03959956.