论文标题：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

期刊：

作者：Paul Porter, Udantha Abeyratne, Vinayak Swarnkar, Jamie Tan, Ti-wan Ng, Joanna M. Brisbane, Deirdre Speldewinde, Jennifer Choveaux, Roneel Sharan, Keegan Kosasih and Phillip Della

发表时间：2019/06/06

数字识别码：10.1186/s12931-019-1046-6

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最近发表在开放获取期刊《呼吸研究》（Respiratory Research）上的一项研究发现，内嵌在一个智能手机应用（app）中的自动咳嗽分析技术或有助于诊断儿童呼吸障碍。

澳大利亚科廷大学和昆士兰大学的研究者们在研究中展示了一款智能手机应用，其诊断哮喘、哮吼、肺炎、下呼吸道疾病和支气管炎的准确率很高（81%-97%）。

该研究的通讯作者Paul Porter博士说：“想要明确诊断儿童所患的呼吸系统疾病到底是哪一种，即便对于有经验的医生而言也并非易事。这个研究展示了新技术、数学概念、机器学习和临床明升手机版可以被成功结合在一起，产生一种应用了多领域知识的全新诊断性检测。”

为了开发这个应用，作者使用了与语音识别类似的技术，训练应用来识别5种不同呼吸系统疾病的典型咳嗽特点。然后研究者用这一应用来为西澳大利亚两家医院中就医的585个孩子的咳嗽声进行分类，这些孩子的年龄介于29天和12岁之间。准确度的评估是通过比较应用给出的诊断和医院给出的诊断，医院的诊断都是由一个小组的儿科医生在看过影像结果、实验室结果、医院记录以及用过所有可用的临床探查手段之后得到的。

作者指出，该研究开发出的这种技术可以在不需要医生亲自问诊的情况下给出诊断，解决了现有远程医疗咨询的一个主要限制因素，可用于远程提供医疗服务。一旦不需要医生进行临床检查，就可以更早开始有针对性的治疗。

Porter博士说：“这个工具不依赖临床检查，因此可以供技能水平不同的健康服务提供者使用。不过我们建议尽可能在使用这一工具时同时咨询临床医生，以最大化临床准确性。”

摘要：

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.

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期刊介绍:

Respiratory Research(,3.751 -, 4.113 -) publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases.

（来源：明升手机版(明升中国)）