大流行
标准化
2019年冠状病毒病(COVID-19)
比例(比率)
风险分析(工程)
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
2019-20冠状病毒爆发
多样性(控制论)
爆发
计算机科学
疾病
数据科学
医疗急救
重症监护医学
医学
传染病(医学专业)
人工智能
病毒学
病理
地理
地图学
操作系统
作者
Mustafa Abumeeiz,Lauren Elliott,Phillip Olla
出处
期刊:Disaster Medicine and Public Health Preparedness
[Cambridge University Press]
日期:2021-10-15
卷期号:16 (5): 2137-2140
被引量:5
摘要
Abstract Due to the coronavirus disease 2019 (COVID-19) pandemic, there is currently a need for accurate, rapid, and easy-to-administer diagnostic tools to help communities manage local outbreaks and assess the spread of disease. The use of artificial intelligence within the domain of breath analysis techniques has shown to have potential in diagnosing a variety of diseases, such as cancer and lung disease, by analyzing volatile organic compounds (VOCs) in exhaled breath. This combined with their rapid, easy-to-use, and noninvasive nature makes them a good candidate for use in diagnosing COVID-19 in large scale public health operations. However, there remains issues with their implementation when it comes to the infrastructure currently available to support their use on a broad scale. This includes issues of standardization, and whether or not a characteristic VOC pattern can be identified for COVID-19. Despite these difficulties, breathalyzers offer potential to assist in pandemic responses and their use should be investigated.
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