Usher Institute, University of Edinburgh, Edinburgh, UK.
Projahnmo Research Foundation, Dhaka, Bangladesh.
J Glob Health. 2022 Apr 23;12:04033. doi: 10.7189/jogh.12.04033. eCollection 2022.
Frontline health care workers use World Health Organization Integrated Management of Childhood Illnesses (IMCI) guidelines for child pneumonia care in low-resource settings. IMCI guideline pneumonia diagnostic criterion performs with low specificity, resulting in antibiotic overtreatment. Digital auscultation with automated lung sound analysis may improve the diagnostic performance of IMCI pneumonia guidelines. This systematic review aims to summarize the evidence on detecting adventitious lung sounds by digital auscultation with automated analysis compared to reference physician acoustic analysis for child pneumonia diagnosis.
In this review, articles were searched from MEDLINE, Embase, CINAHL Plus, Web of Science, Global Health, IEEExplore database, Scopus, and the ClinicalTrial.gov databases from the inception of each database to October 27, 2021, and reference lists of selected studies and relevant review articles were searched manually. Studies reporting diagnostic performance of digital auscultation and/or computerized lung sound analysis compared against physicians' acoustic analysis for pneumonia diagnosis in children under the age of 5 were eligible for this systematic review. Retrieved citations were screened and eligible studies were included for extraction. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. All these steps were independently performed by two authors and disagreements between the reviewers were resolved through discussion with an arbiter. Narrative data synthesis was performed.
A total of 3801 citations were screened and 46 full-text articles were assessed. 10 studies met the inclusion criteria. Half of the studies used a publicly available respiratory sound database to evaluate their proposed work. Reported methodologies/approaches and performance metrics for classifying adventitious lung sounds varied widely across the included studies. All included studies except one reported overall diagnostic performance of the digital auscultation/computerised sound analysis to distinguish adventitious lung sounds, irrespective of the disease condition or age of the participants. The reported accuracies for classifying adventitious lung sounds in the included studies varied from 66.3% to 100%. However, it remained unclear to what extent these results would be applicable for classifying adventitious lung sounds in children with pneumonia.
This systematic review found very limited evidence on the diagnostic performance of digital auscultation to diagnose pneumonia in children. Well-designed studies and robust reporting are required to evaluate the accuracy of digital auscultation in the paediatric population.
在资源匮乏的环境中,一线医护人员使用世界卫生组织儿童疾病综合管理(IMCI)指南来治疗儿童肺炎。IMCI 指南中肺炎诊断标准的特异性较低,导致抗生素过度治疗。使用数字听诊和自动肺部声音分析可能会提高 IMCI 肺炎指南的诊断性能。本系统评价旨在总结使用数字听诊和自动分析检测肺部异常声音与参照医师声学分析比较,用于儿童肺炎诊断的证据。
本综述从 MEDLINE、Embase、CINAHL Plus、Web of Science、全球卫生、IEEExplore 数据库、Scopus 和 ClinicalTrial.gov 数据库中搜索了从每个数据库建立到 2021 年 10 月 27 日的文章,并手动搜索了选定研究和相关综述文章的参考文献列表。本系统评价纳入了报告数字听诊和/或计算机化肺部声音分析与医生声学分析比较,用于诊断 5 岁以下儿童肺炎的诊断性能的研究。检索到的引文经过筛选,符合条件的研究被纳入提取。使用诊断准确性研究质量评估工具-2(QUADAS-2)评估偏倚风险。所有这些步骤均由两位作者独立完成,审稿人之间的分歧通过与仲裁人讨论解决。采用叙述性数据综合法。
共筛选出 3801 条引文,46 篇全文文章进行了评估。10 项研究符合纳入标准。其中一半的研究使用了公共呼吸声音数据库来评估他们提出的工作。纳入研究中报告的用于分类肺部异常声音的方法学/方法和性能指标差异很大。除一项研究外,所有纳入的研究均报告了数字听诊/计算机声音分析总体诊断性能,以区分肺部异常声音,而不论参与者的疾病状况或年龄如何。纳入研究中分类肺部异常声音的报告准确率从 66.3%到 100%不等。然而,尚不清楚这些结果在多大程度上适用于分类儿童肺炎的肺部异常声音。
本系统评价发现,关于数字听诊诊断儿童肺炎的诊断性能的证据非常有限。需要设计良好的研究和可靠的报告来评估数字听诊在儿科人群中的准确性。