论文标题
Viviufy：众包和临床数据集的全球适用性，用于AI从咳嗽中检测COVID-19
Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough
论文作者
论文摘要
快速且负担得起的COVID-19感染测试方法对于降低感染率和防止医疗机构变得不堪重负至关重要。当前检测COVID-19的方法需要与昂贵的套件进行面对面的测试，这些套件并不总是容易访问。这项研究表明，在来自世界各地的智能手机上记录并获取的众包音频样本可用于开发一种基于AI的方法，该方法可准确预测COVID-19感染，ROC-AUC为77.1％（75.2％-78.3％）。此外，我们表明我们的方法能够推广到拉丁美洲的众包音频样本和南亚的临床样本，而无需使用这些地区的特定样本进行进一步的培训。随着收集更多的众包数据，可以使用各种呼吸道音频样本来实施进一步的开发，以创建基于咳嗽分析的机器学习（ML）解决方案，以用于COVID-19检测，该检测可能可能在临床和非临床环境中全球范围内概括为全球范围。
Rapid and affordable methods of testing for COVID-19 infections are essential to reduce infection rates and prevent medical facilities from becoming overwhelmed. Current approaches of detecting COVID-19 require in-person testing with expensive kits that are not always easily accessible. This study demonstrates that crowdsourced cough audio samples recorded and acquired on smartphones from around the world can be used to develop an AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1% (75.2%-78.3%). Furthermore, we show that our method is able to generalize to crowdsourced audio samples from Latin America and clinical samples from South Asia, without further training using the specific samples from those regions. As more crowdsourced data is collected, further development can be implemented using various respiratory audio samples to create a cough analysis-based machine learning (ML) solution for COVID-19 detection that can likely generalize globally to all demographic groups in both clinical and non-clinical settings.
