气候变化领域集成服务门户(CCPortal): Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation

CCPortal

DOI	10.1073/pnas.2019893118
	Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation
	Mendels D.-A.; Dortet L.; Emeraud C.; Oueslati S.; Girlich D.; Ronat J.-B.; Bernabeu S.; Bahi S.; Atkinson G.J.H.; Naas T.
发表日期	2021
ISSN	00278424
卷号	118 期号:12
英文摘要	Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible “bands” of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	SARS-CoV-2 \| machine learning \| smartphone application
语种	英语
scopus关键词	hemoglobin; Article; artificial intelligence; automation; biosafety; clinical outcome; convolutional neural network; coronavirus disease 2019; COVID-19 serological testing; diagnostic accuracy; diagnostic test; diagnostic test accuracy study; human; interpretation bias; lateral flow immunoassay; machine learning; nonhuman; pandemic; performance measurement system; polymerase chain reaction; prediction; priority journal; quality control; rapid diagnostic test; self evaluation; Severe acute respiratory syndrome coronavirus 2; virus classification; diagnosis; machine learning; mobile application; COVID-19; COVID-19 Serological Testing; Humans; Machine Learning; Mobile Applications; SARS-CoV-2
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180198
作者单位	xRapid-Group, Aix en Provence, 13100, France; Bacteriology-Hygiene Unit, Assistance Publique/Hôpitaux de Paris, Bicêtre Hospital, Le Kremlin-Bicêtre, 94275, France; INSERM Public Health Research, UMR 1184, RESIST Unit Paris-Saclay University, Faculty of Medicine, Le Kremlin-Bicêtre, 94270, France; Associated French National Reference Center for Antibiotic Resistance: Carbapenemase-Producing Enterobacteriaceae, Le Kremlin-Bicêtre, 94270, France; Mini-Lab Project, Medecins Sans Frontière, Paris, 75019, France
推荐引用方式 GB/T 7714	Mendels D.-A.,Dortet L.,Emeraud C.,et al. Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation[J],2021,118(12).
APA	Mendels D.-A..,Dortet L..,Emeraud C..,Oueslati S..,Girlich D..,...&Naas T..(2021).Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.Proceedings of the National Academy of Sciences of the United States of America,118(12).
MLA	Mendels D.-A.,et al."Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation".Proceedings of the National Academy of Sciences of the United States of America 118.12(2021).