Maor Elad, Tsur Nir, Barkai Galia, Meister Ido, Makmel Shmuel, Friedman Eli, Aronovich Daniel, Mevorach Dana, Lerman Amir, Zimlichman Eyal, Bachar Gideon
Leviev Heart Center, Sheba Medical Center, Tel Hashomer, Israel.
Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel.
Mayo Clin Proc Innov Qual Outcomes. 2021 Jun;5(3):654-662. doi: 10.1016/j.mayocpiqo.2021.05.007. Epub 2021 May 14.
To investigate the association of voice analysis with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
A vocal biomarker, a unitless scalar with a value between 0 and 1, was developed based on 434 voice samples. The biomarker training was followed by a prospective, multicenter, observational study. All subjects were tested for SARS-CoV-2, had their voice recorded to a smartphone application, and gave their informed consent to participate in the study. The association of SARS-CoV-2 infection with the vocal biomarker was evaluated.
The final study population included 80 subjects with a median age of 29 [range, 23 to 36] years, of whom 68% were men. Forty patients were positive for SARS-CoV-2. Infected patients were 12 times more likely to report at least one symptom (odds ratio, 11.8; <.001). The vocal biomarker was significantly higher among infected patients (OR, 0.11; 95% CI, 0.06 to 0.17 vs OR, 0.19; 95% CI, 0.12 to 0.3; =.001). The area under the receiver operating characteristic curve evaluating the association of the vocal biomarker with SARS-CoV-2 status was 72%. With a biomarker threshold of 0.115, the results translated to a sensitivity and specificity of 85% (95% CI, 70% to 94%) and 53% (95% CI, 36% to 69%), respectively. When added to a self-reported symptom classifier, the area under the curve significantly improved from 0.775 to 0.85.
Voice analysis is associated with SARS-CoV-2 status and holds the potential to improve the accuracy of self-reported symptom-based screening tools. This pilot study suggests a possible role for vocal biomarkers in screening for SARS-CoV-2-infected subjects.
研究语音分析与严重急性呼吸综合征冠状病毒2(SARS-CoV-2)感染之间的关联。
基于434份语音样本开发了一种声音生物标志物,这是一个取值在0到1之间的无量纲标量。在生物标志物训练之后进行了一项前瞻性、多中心观察性研究。所有受试者均接受SARS-CoV-2检测,通过智能手机应用程序记录其声音,并给予知情同意以参与研究。评估了SARS-CoV-2感染与声音生物标志物之间的关联。
最终研究人群包括80名受试者,中位年龄为29岁[范围为23至36岁],其中68%为男性。40名患者SARS-CoV-2检测呈阳性。感染患者报告至少一种症状的可能性高12倍(比值比,11.8;<.001)。感染患者的声音生物标志物显著更高(比值比,0.11;95%置信区间,0.06至0.17对比比值比,0.19;95%置信区间,0.12至0.3;=.001)。评估声音生物标志物与SARS-CoV-2状态之间关联的受试者工作特征曲线下面积为72%。生物标志物阈值为0.115时,结果转化为敏感性和特异性分别为85%(95%置信区间,70%至94%)和53%(95%置信区间,36%至69%)。当添加到自我报告症状分类器中时,曲线下面积从0.775显著提高到0.85。
语音分析与SARS-CoV-2状态相关,并且有可能提高基于自我报告症状的筛查工具的准确性。这项初步研究表明声音生物标志物在筛查SARS-CoV-2感染受试者方面可能发挥作用。