[论文解读] Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic

Brian Subirana, Ferran Hueto+11 more|arXiv (Cornell University)|Apr 10, 2020

COVID-19 diagnosis using AI被引用 63

一句话总结

本文提出了一种开放的集体AI方法，使用咳嗽音频迁移学习来诊断COVID-19，并概述在多个站点的验证计划，以及用于纵向测试和ICU优先级排序的工具。

ABSTRACT

Just like your phone can detect what song is playing in crowded spaces, we show that Artificial Intelligence transfer learning algorithms trained on cough phone recordings results in diagnostic tests for COVID-19. To gain adoption by the health care community, we plan to validate our results in a clinical trial and three other venues in Mexico, Spain and the USA . However, if we had data from other on-going clinical trials and volunteers, we may do much more. For example, for confirmed stay-at-home COVID-19 patients, a longitudinal audio test could be developed to determine contact-with-hospital recommendations, and for the most critical COVID-19 patients a success ratio forecast test, including patient clinical data, to prioritize ICU allocation. As a challenge to the engineering community and in the context of our clinical trial, the authors suggest distributing cough recordings daily, hoping other trials and crowdsourcing users will contribute more data. Previous approaches to complex AI tasks have either used a static dataset or were private efforts led by large corporations. All existing COVID-19 trials published also follow this paradigm. Instead, we suggest a novel open collective approach to large-scale real-time health care AI. We will be posting updates at https://opensigma.mit.edu. Our personal view is that our approach is the right one for large scale pandemics, and therefore is here to stay - will you join?

研究动机与目标

• 在COVID-19大流行期间，推动开发一种新的AI范式以帮助医护专业人员。
• 提出将咳嗽音频的迁移学习作为COVID-19诊断信号。
• 倡导开放的、实时的数据共享和众包数据收集，以加速验证。
• 描述在墨西哥、西班牙和美国的计划临床试验和多地点验证。

提出的方法

• 使用基于咳嗽录音训练的AI迁移学习来创建COVID-19诊断测试。
• 通过临床试验和三个国际场地来规划验证。
• 为确诊居家患者提出纵向音频测试，以为医院联系指导提供信息。
• 提出一个以患者为中心的预测工具，用于结合临床数据的ICU排序优先级。
• 倡导对大规模实时医疗AI开放的集体式方法。

实验结果

研究问题

• RQ1 能否使用从移动设备获得的咳嗽音频，通过迁移学习准确诊断COVID-19？
• RQ2 在多个国际场所验证AI驱动的咳嗽诊断的可行性和收益是什么？
• RQ3 纵向音频数据如何为COVID-19患者的治疗决策和医院资源分配提供信息？
• RQ4 开放、众包数据模型在大流行期间对快速AI开发的潜在影响是什么？

主要发现

• 据作者声称，基于咳嗽录音的AI迁移学习可以产生COVID-19诊断测试。
• 提出一个临床试验和国际部署计划以验证该方法。
• 描述了一个针对居家患者的纵向音频测试概念，以指导医院联系决策。
• 提出一个针对危重患者的成功率预测测试，当与临床数据结合时用于优先分配ICU。
• 作者倡导对大规模实时医疗AI和数据共享采取开放的集体式方法。