A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children
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DOI：
10.1186/s12931-019-1046-6
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发表时间：
2019-06-06
影响因子：
5.8
通讯作者：
Della, Phillip
中科院分区：
文献类型：
--
作者：
Porter, Paul;Abeyratne, Udantha;Della, Phillip
背景：小儿呼吸系统疾病的鉴别诊断是困难和次优的。现有的诊断算法与显著的错误率相关，导致误诊、抗生素使用不当以及不可接受的发病率和死亡率。声学工程和人工智能的最新进展在基于声音分析的呼吸系统疾病识别方面显示出了希望，减少了对诊断支持服务和临床专业知识的依赖。我们提出了一项使用自动咳嗽声分析仪诊断儿科呼吸系统疾病的准确性研究结果。方法：记录典型临床环境下的咳嗽声，并采用前5次咳嗽声进行分析。使用咳嗽数据和来自患者/父母报告病史的多达五种症状输入进行分析。将自动咳嗽分析仪诊断与儿科医生小组在审查医院图表和所有可用调查后达成的共识临床诊断进行比较。结果：共纳入585例29天至12岁的受试者进行分析。自动分析仪与临床参考之间的阳性百分比和阴性百分比一致性值如下：哮喘（97,91%）；肺炎（87,85%）；下呼吸道疾病（83,82%）；组（85,82%）；细支气管炎（84,81%）。结论：该技术可作为儿童常见呼吸系统疾病的高水平诊断辅助手段。试验注册：澳大利亚和新西兰临床试验注册中心（回顾性）- ACTRN12618001521213: 11.09。2018.
Background: The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser.Methods: We recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations.Results: A total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%). Conclusion: The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders.Trial registration: Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213: 11.09. 2018.