PDF(Pubmed)
Abstract：
To assess the effectiveness of 3 novel lung ultrasound (LUS)-based parameters: Pneumonia Score and Lung Staging for pneumonia staging and COVID Index, indicating the probability of SARS-CoV-2 infection.
Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol. For each zone, a Pneumonia score from 0 to 4 was assigned by the algorithm and by an expert operator (kept blind with respect to the algorithm results) on the basis of the identified imaging signs and the patient Lung Staging was derived as the highest observed score. The output of the operator was considered as the ground truth. The algorithm calculated also the COVID Index by combining the automatically identified LUS markers with the questionnaire answers and compared with the nasopharyngeal swab results.
Overall, 556 patients were analysed. A high agreement between the algorithm assignments and the expert operator evaluations was observed, both for Pneumonia Score and Lung Staging, with the latter having sensitivity and specificity over 92% both in the discrimination between healthy/sick patients and between sick patients with mild/severe pneumonia. Regarding the COVID Index, an area under the curve of 0.826 was observed for the classification of patients with/without SARS-CoV-2.
The proposed methodology allowed the identification and staging of patients suffering from pneumonia with high accuracy. Moreover, it provided the probability of being infected by SARS-CoV-2.
Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol. For each zone, a Pneumonia score from 0 to 4 was assigned by the algorithm and by an expert operator (kept blind with respect to the algorithm results) on the basis of the identified imaging signs and the patient Lung Staging was derived as the highest observed score. The output of the operator was considered as the ground truth. The algorithm calculated also the COVID Index by combining the automatically identified LUS markers with the questionnaire answers and compared with the nasopharyngeal swab results.
Overall, 556 patients were analysed. A high agreement between the algorithm assignments and the expert operator evaluations was observed, both for Pneumonia Score and Lung Staging, with the latter having sensitivity and specificity over 92% both in the discrimination between healthy/sick patients and between sick patients with mild/severe pneumonia. Regarding the COVID Index, an area under the curve of 0.826 was observed for the classification of patients with/without SARS-CoV-2.
The proposed methodology allowed the identification and staging of patients suffering from pneumonia with high accuracy. Moreover, it provided the probability of being infected by SARS-CoV-2.
摘要：
评估3种新的基于肺部超声（LUS）的参数的有效性：肺炎评分和肺分期肺炎分期和COVID指数，表明SARS-CoV-2感染的可能性。
急诊收治的有潜在肺炎相关症状的成年患者，对健康志愿者和来自在线可访问数据库的临床病例进行了评估.患者接受了临床流行病学调查问卷和LUS采集，遵循14区协议。对于每个区域，根据识别出的影像学征象,算法和专家操作员(对算法结果保持盲态)将肺炎得分为0~4分,并得出患者肺部分期作为观察到的最高得分.操作员的输出被认为是基础事实。该算法还通过将自动识别的LUS标记与问卷答案相结合来计算COVID指数，并与鼻咽拭子结果进行比较。
总的来说，对556例患者进行了分析。观察到算法分配和专家操作员评估之间的高度一致性，肺炎评分和肺分期，后者在区分健康/患病患者和轻度/重度肺炎患者之间具有超过92％的敏感性和特异性。关于COVID指数，对于有/没有SARS-CoV-2的患者的分类，曲线下面积为0.826。
所提出的方法可以高精度地识别和分期患有肺炎的患者。此外，它提供了被SARS-CoV-2感染的可能性。
急诊收治的有潜在肺炎相关症状的成年患者，对健康志愿者和来自在线可访问数据库的临床病例进行了评估.患者接受了临床流行病学调查问卷和LUS采集，遵循14区协议。对于每个区域，根据识别出的影像学征象,算法和专家操作员(对算法结果保持盲态)将肺炎得分为0~4分,并得出患者肺部分期作为观察到的最高得分.操作员的输出被认为是基础事实。该算法还通过将自动识别的LUS标记与问卷答案相结合来计算COVID指数，并与鼻咽拭子结果进行比较。
总的来说，对556例患者进行了分析。观察到算法分配和专家操作员评估之间的高度一致性，肺炎评分和肺分期，后者在区分健康/患病患者和轻度/重度肺炎患者之间具有超过92％的敏感性和特异性。关于COVID指数，对于有/没有SARS-CoV-2的患者的分类，曲线下面积为0.826。
所提出的方法可以高精度地识别和分期患有肺炎的患者。此外，它提供了被SARS-CoV-2感染的可能性。
