Rapid and affordable methods of testing for COVID-19 infection are essential to manage infection rates and prevent medical facilities from becoming overwhelmed. This study demonstrates that crowdsourced cough audio samples acquired on smartphones across the world and paired with COVID-19 status labels can be used to develop an AI algorithm that accurately predicts COVID-19 infection with an ROC-AUC of 77.1% (75.2%-78.3%). Furthermore, this AI algorithm is able to generalize to crowdsourced samples from Latin America and clinical samples from South Asia, without further training using the specific samples. As more crowdsourced data is collected, further development can be implemented using various respiratory audio samples to create a cough analysis-based AI solution for COVID-19 detection that can likely generalize globally to all demographic groups in both clinical and non-clinical settings.

翻译：对COVID-19感染进行快速和负担得起的检测方法对于管理感染率和防止医疗设施不堪重负至关重要。这项研究表明,可以使用世界各地智能手机上获得的与COVID-19地位标签相配的众源咳嗽声样本来开发一种AI算法,精确预测COVID-19感染,而ROC-AUC的感染率为77.1%(75.2-78.3%)。此外,这种AI算法可以将来自拉丁美洲的众源样本和来自南亚的临床样本加以推广,而无需使用特定样本进行进一步的培训。随着收集到更多的众源数据,可以使用各种呼吸道声样本进行进一步开发,为COVID-19检测建立基于咳嗽分析的AI解决方案,从而有可能将临床和非临床环境中的所有人口群体推广到全球范围。