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<p><b>Subgroup analysis of performance of radiologist and software CAD1 and CAD2 at identifying COVID-19 in comparison to baseline molecular testing grouped by gender</b> Female (A) and male (B). AUC: Area under Curve, RadRS: Radiologist reference standard for COVID-19. CAD1: Compu...

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Main Author:	Sam Linsen (20606266) (author)
Other Authors:	Aurélie Kamoun (13235082) (author), Andrews Gunda (5537063) (author), Tamara Mwenifumbo (20606269) (author), Chancy Chavula (17445772) (author), Lindiwe Nchimunya (20606272) (author), Yucheng Tsai (20606275) (author), Namwaka Mulenga (20606278) (author), Godfrey Kadewele (20606281) (author), Eunice Nahache Kajombo (20606284) (author), Veronica Sunkutu (846233) (author), Jane Shawa (20606287) (author), Rigveda Kadam (8374446) (author), Matthew Arentz (122414) (author)
Published:	2025
Subjects:	Medicine Neuroscience Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified representative training data ongoing data collection maintain diagnostic accuracy high prevalent population effectiveness varies significantly 65 &# 8211 52 &# 8211 19 laboratory results 19 case finding cad show promise support effective triage settings regarding covid highly portable cxr showed inferior performance one cad software study evaluating cxrs 671 individuals evaluated 19 cad performance zambia </ p radiologists interpreted cxrs showed performance cad software evaluated performance study highlights optimize performance 19 cxr individuals evaluated cad ), abnormal cxrs sars cov retrospective cross little evidence key populations june 2022 january 2021 identifying covid future pandemics findings suggest expert radiologist distinguishing normal differentiating normal diagnosing covid current ai chest radiographs better prepare 75 )), 63 )). 2 infection
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S2 Fig -

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