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Machine Learning to Support Visual Auditing of Home-based Lateral Flow Immunoassay Self-Test Results for SARS-CoV-2 Antibodies

dc.contributor.authorWong, Nathan Chun Kin
dc.contributor.authorMeshkinfamfard, Sepehr
dc.contributor.authorTurbe, Valerian
dc.contributor.authorWhitaker, Matthew
dc.contributor.authorMoshe, Maya
dc.contributor.authorBardanzellu, Alessia
dc.contributor.authorDai, Tianhong
dc.contributor.authorPignatelli, Eduardo
dc.contributor.authorBarclay, Wendy
dc.contributor.authorDarzi, Ara
dc.contributor.authorElliott, Paul
dc.contributor.authorWard, Helen
dc.contributor.authorTanaka, Reiko
dc.contributor.authorCooke, Graham
dc.contributor.authorMcKendry, Rachel
dc.contributor.authorAtchison, Christina
dc.contributor.authorBharath, Anil A.
dc.date.accessioned2024-05-13T23:12:51Z
dc.date.available2024-05-13T23:12:51Z
dc.date.issued2022-12
dc.identifier221356712
dc.identifierb800ce5d-2cdd-4006-9fb0-d894afb08486
dc.identifier.citationWong , N C K , Meshkinfamfard , S , Turbe , V , Whitaker , M , Moshe , M , Bardanzellu , A , Dai , T , Pignatelli , E , Barclay , W , Darzi , A , Elliott , P , Ward , H , Tanaka , R , Cooke , G , McKendry , R , Atchison , C & Bharath , A A 2022 , ' Machine Learning to Support Visual Auditing of Home-based Lateral Flow Immunoassay Self-Test Results for SARS-CoV-2 Antibodies ' , Communications Medicine , vol. 2 , 78 . https://doi.org/10.1038/s43856-022-00146-zen
dc.identifier.issn2730-664X
dc.identifier.otherORCID: /0000-0001-8904-1551/work/122288875
dc.identifier.urihttp://aura-test.abdn.ac.uk/handle/2164/20296
dc.descriptionThis work was funded by the Department of Health and Social Care in England. The content of this manuscript and decision to submit for publication were the responsibility of the authors and the funders had no role in these decisions. H.W. is a NIHR Senior Investigator and acknowledges support from NIHR Biomedical Research Centre of Imperial College NHS Trust, NIHR School of Public Health Research, NIHR Applied Research Collaborative North West London, Wellcome Trust (UNS32973). G.C. is supported by an NIHR Professorship and the NIHR Imperial Biomedical Research Centre. W.B. is the Action Medical Research Professor and A.D. is an NIHR senior investigator. P.E. is Director of the MRC Centre for Environment and Health (MR/L01341X/1, MR/S019669/1). P.E. acknowledges support from the NIHR Imperial Biomedical Research Centre and the NIHR HPRUs in Chemical and Radiation Threats and Hazards, and Environmental Exposures and Health, the British Heart Foundation Centre for Research Excellence at Imperial College London (RE/18/4/34215), the UK Dementia Research Institute at Imperial (MC_PC_17114) and Health Data Research UK (HDR UK). R.A.M., V.T. and S.M. were funded by the i-sense EPSRC IRC in Agile Early Warning Sensing Systems for Infectious Diseases and Antimicrobial Resistance and associated COVID Plus Award (no. EP/R00529X/1). R.A.M and S.M. were supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. This work was also supported by the NTU-Imperial Research Collaboration Fund and the EPSRC Impact Acceleration Award (EP/R511547/1). We thank key collaborators on this work—Ipsos MORI: Stephen Finlay and Duncan Peskett; School of Public Health at Imperial College London: Eric Johnson and Rob Elliot; the Imperial Patient Experience Research Centre and the REACT Public Advisory Panel.en
dc.format.extent10
dc.format.extent1349012
dc.language.isoeng
dc.relation.ispartofCommunications Medicineen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectQA76 Computer softwareen
dc.subjectRA Public aspects of medicineen
dc.subjectWellcome Trusten
dc.subjectUNS32973en
dc.subjectNational Institute for Health Research (NIHR)en
dc.subjectMedical Research Council (MRC)en
dc.subjectMR/L01341X/1en
dc.subjectMR/S019669/1en
dc.subjectBritish Heart Foundationen
dc.subjectRE/18/4/34215en
dc.subjectEngineering and Physical Sciences Research Council (EPSRC)en
dc.subjectEP/R00529X/1en
dc.subjectEP/R511547/1en
dc.subjectSupplementary Dataen
dc.subject.lccQA76en
dc.subject.lccRAen
dc.titleMachine Learning to Support Visual Auditing of Home-based Lateral Flow Immunoassay Self-Test Results for SARS-CoV-2 Antibodiesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.description.statusPeer revieweden
dc.identifier.doihttps://doi.org/10.1038/s43856-022-00146-z


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