Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.
Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.
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DOI：
10.1073/pnas.2019893118
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发表时间：
2021-03-23
影响因子：
11.1
通讯作者：
Naas T
中科院分区：
文献类型：
--
作者：
Mendels DA;Dortet L;Emeraud C;Oueslati S;Girlich D;Ronat JB;Bernabeu S;Bahi S;Atkinson GJH;Naas T
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.
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影响因子：
82.9
作者：
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通讯作者：
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DOI：
10.1093/jac/dkx521
发表时间：
2018-04-01
期刊：
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影响因子：
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发表时间：
2018-11-19
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
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通讯作者：
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影响因子：
22.7
作者：
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影响因子：
9.4
作者：
Dortet L;Ronat JB;Vauloup-Fellous C;Langendorf C;Mendels DA;Emeraud C;Oueslati S;Girlich D;Chauvin A;Afdjei A;Bernabeu S;Le Pape S;Kallala R;Rochard A;Verstuyft C;Fortineau N;Roque-Afonso AM;Naas T
通讯作者：
Naas T