Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.
Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.
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DOI：
10.1186/s12890-021-01467-w
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发表时间：
2021-03-24
影响因子：
3.1
通讯作者：
Siebert JN
中科院分区：
文献类型：
--
作者：
Glangetas A;Hartley MA;Cantais A;Courvoisier DS;Rivollet D;Shama DM;Perez A;Spechbach H;Trombert V;Bourquin S;Jaggi M;Barazzone-Argiroffo C;Gervaix A;Siebert JN
肺部听诊是临床诊断呼吸系统疾病的基础。然而，听诊是一种主观实践，用户之间的解释差异很大。听诊采集和解释的数字化是诊断和监测传染病（如冠状病毒19疾病（COVID-19））的一种特别有前途的策略，其中自动分析可以帮助分散护理并更好地为远程医疗决策提供信息。该方案描述了COVID-19分诊点肺部听诊的标准化收集，以及诊断和预后建模的深度学习方法，以供未来纳入以人类专家解释为基准的智能自主听诊器中。从2020年10月开始，将在筛查中心和日内瓦大学医院内科住院患者中招募总计1000名年龄≥ 16岁且符合COVID-19检测标准的连续患者。COVID-19是通过鼻咽拭子上的RT-PCR诊断的，COVID阳性患者被随访直到结果（即，出院、住院、插管和/或死亡）。在入选时，收集人口统计学和临床数据，例如年龄、性别、病史以及当前发作的体征和症状。此外，将使用数字听诊器记录每例患者6个胸部部位的肺部听诊。使用卷积神经网络（CNN）和支持向量机分类器的深度学习算法（DeepBreath）将在这些录音上进行训练，以获得诊断（COVID阳性与阴性）和风险分层类别（轻度至重度）的自动预测。该模型的性能将与肺音随机子集的人类预测基线进行比较，其中要求盲医生将音频分类为相同类别。这种方法具有广泛的潜力，可以在各级医疗保健中评估COVID-19的肺部听诊，特别是在分散的分诊和监测背景下。试验注册：PB_2016-00500，SwissEthics。于二零二零年四月六日注册。在线版本包含补充材料，可通过10.1186/s12890-021-01467-w获得。
Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation. A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories. This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring. Trial registration: PB_2016-00500, SwissEthics. Registered on 6 April 2020. The online version contains supplementary material available at 10.1186/s12890-021-01467-w.
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影响因子：
12.4
作者：
Liu, Fengjun;Zhang, Qi;Shi, Yuxin
通讯作者：
Shi, Yuxin
DOI：
10.1164/rccm.201807-1351so
发表时间：
2019-02-01
影响因子：
24.7
作者：
Bhatt, Surya P.;Washko, George R.;Lynch, David A.
通讯作者：
Lynch, David A.
影响因子：
5.9
作者：
Francone, Marco;Iafrate, Franco;Catalano, Carlo
通讯作者：
Catalano, Carlo
影响因子：
3.7
作者：
Hafke-Dys, Honorata;Breborowicz, Anna;Biniakowski, Adam
通讯作者：
Biniakowski, Adam
影响因子：
5.3
作者：
Abbas, Ali;Fahim, Atef
通讯作者：
Fahim, Atef