Background: The inability to test at scale has become humanity's Achille's heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture. Results: Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19 coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to channel clinical-testing and treatment to those who need it the most, thereby saving more lives.

中文翻译：

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AI4COVID-19：人工智能通过应用程序从咳嗽样本中对 COVID-19 进行初步诊断

背景：在与 COVID-19 大流行的持续战争中，无法进行大规模测试已成为人类的致命弱点。可扩展的筛选工具将改变游戏规则。在先前基于咳嗽的呼吸道疾病诊断工作的基础上，我们提出、开发和测试了一种人工智能 (AI) 驱动的 COVID-19 感染筛查解决方案，该解决方案可通过智能手机应用程序部署。这款名为 AI4COVID-19 的应用程序会记录三个 3 秒的咳嗽声并将其发送到运行在云端的 AI 引擎，并在两分钟内返回结果。方法：咳嗽是三十多种非 COVID-19 相关疾病的症状。这使得仅通过咳嗽诊断 COVID-19 感染成为极具挑战性的多学科问题。我们通过研究与其他呼吸道感染相比，由 COVID-19 感染引起的呼吸系统病理形态学改变的独特性来解决这个问题。为了克服 COVID-19 咳嗽训练数据短缺，我们利用迁移学习。为了减少由问题的复杂维度引起的误诊风险，我们利用了以多管齐下的中介为中心的规避风险的 AI 架构。结果：结果显示 AI4COVID-19 可以区分 COVID-19 咳嗽和几种非 COVID-19 咳嗽。准确性足以鼓励大规模收集标记的咳嗽数据，以衡量 AI4COVID-19 的泛化能力。AI4COVID-19 不是临床级测试工具。相反，它提供了一种任何人都可以随时随地部署的筛选工具。