Objectives Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images. Design A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians. Setting Two tertiary Canadian hospitals. Participants 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE). Results The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01. Conclusions A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

中文翻译：

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卷积神经网络的发展，以区分在肺部超声上出现的相似病理性B线的病因：一项深度学习研究

目的肺超声（LUS）是一种便携式的低成本呼吸成像工具，但受到用户依赖性和缺乏诊断特异性的挑战。LUS实施的优势是否可以与深度学习（DL）技术配对使用，以匹配或超过人类在相似出现的病理性LUS图像中的诊断特异性尚不清楚。设计在具有不同病因的B线的LUS图像上训练了卷积神经网络（CNN）。使用10％的数据保留集验证了CNN的诊断性能，并将其与接受调查的具有LUS能力的医生进行了比较。设置两家加拿大三级医院。参与者来自243名患有（1）COVID-19（COVID），非COVID急性呼吸窘迫综合征（NCOVID）或（3）静水性肺水肿（HPE）的不同患者的612条LUS视频（121 381帧）。结果在独立数据集上经过训练的CNN性能显示出能够区分COVID（接收器工作特征曲线（AUC）1.0下的区域），NCOVID（AUC 0.934）和HPE（AUC 1.0）病理的能力。这显着优于医师的能力（对于COVID，NCOVID和HPE类，AUC分别为0.697、0.704、0.967），p <0.01。结论DL模型可以区分人类无法区分的相似出现的LUS病理，包括COVID-19。人类与模型之间的性能差距表明，超声图像中可能存在亚可见的生物标志物，值得进行多中心研究。934）和HPE（AUC 1.0）病理。这显着优于医师的能力（对于COVID，NCOVID和HPE类，AUC分别为0.697、0.704、0.967），p <0.01。结论DL模型可以区分人类无法区分的相似出现的LUS病理，包括COVID-19。人类与模型之间的性能差距表明，超声图像中可能存在亚可见的生物标志物，值得进行多中心研究。934）和HPE（AUC 1.0）病理。这显着优于医师的能力（对于COVID，NCOVID和HPE类，AUC分别为0.697、0.704、0.967），p <0.01。结论DL模型可以区分人类无法区分的相似出现的LUS病理，包括COVID-19。人类与模型之间的性能差距表明，超声图像中可能存在亚可见的生物标志物，值得进行多中心研究。