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Abstract
Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.
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1 Radboud University Medical Center, Nijmegen, The Netherlands (GRID:grid.10417.33) (ISNI:0000 0004 0444 9382)
2 SolidarMed, Partnerships for Health, Maseru, Lesotho (GRID:grid.10417.33)
3 Human Sciences Research Council, Centre for Community Based Research, Pietermaritzburg, South Africa (GRID:grid.417715.1) (ISNI:0000 0001 0071 1142); University of the Witwatersrand, SAMRC/WITS Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, Johannesburg, South Africa (GRID:grid.11951.3d) (ISNI:0000 0004 1937 1135)
4 Human Sciences Research Council, Centre for Community Based Research, Pietermaritzburg, South Africa (GRID:grid.417715.1) (ISNI:0000 0001 0071 1142)
5 University Hospital Basel, Department of Infectious Diseases and Hospital Epidemiology, Basel, Switzerland (GRID:grid.410567.1); SolidarMed, Partnerships for Health, Lucerne, Switzerland (GRID:grid.410567.1)
6 Swiss Tropical and Public Health Institute, Allschwil, Switzerland (GRID:grid.416786.a) (ISNI:0000 0004 0587 0574); University of Basel, Basel, Switzerland (GRID:grid.6612.3) (ISNI:0000 0004 1937 0642)