Machine Learning for Determining Lateral Flow Device Results in Asymptomatic Population: A Diagnostic Accuracy Study

2021 ◽  
Author(s):  
AI LFD Consortium ◽  
Andrew D. Beggs
Keyword(s):  
Machine Learning ◽  
Diagnostic Accuracy ◽  
Lateral Flow ◽  
Diagnostic Accuracy Study ◽  
Lateral Flow Device ◽  
Flow Device ◽  
Accuracy Study

Machine learning volumetry of ischemic brain lesions on CT after thrombectomy—prospective diagnostic accuracy study in ischemic stroke patients

2020 ◽  
Vol 62 (10) ◽  
pp. 1239-1245
Author(s):  
Jiri Kral ◽  
Martin Cabal ◽  
Linda Kasickova ◽  
Jaroslav Havelka ◽  
Tomas Jonszta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Diagnostic Accuracy ◽  
Brain Lesions ◽  
Diagnostic Accuracy Study ◽  
Stroke Patients ◽  
Ischemic Brain ◽  
Accuracy Study

scholarly journals Evaluation of Fingerstick Cryptococcal Antigen Lateral Flow Assay in HIV-Infected Persons: A Diagnostic Accuracy Study: Figure 1.

2015 ◽  
Vol 61 (3) ◽  
pp. 464-467 ◽  
Author(s):  
Darlisha A. Williams ◽  
Tadeo Kiiza ◽  
Richard Kwizera ◽  
Reuben Kiggundu ◽  
Sruti Velamakanni ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Lateral Flow ◽  
Lateral Flow Assay ◽  
Diagnostic Accuracy Study ◽  
Cryptococcal Antigen ◽  
Accuracy Study

scholarly journals SARS-CoV-2 lateral flow assays for possible use in national covid-19 seroprevalence surveys (React 2): diagnostic accuracy study

BMJ ◽  
2021 ◽  
pp. n423 ◽  
Author(s):  
Maya Moshe ◽  
Anna Daunt ◽  
Barnaby Flower ◽  
Bryony Simmons ◽  
Jonathan C Brown ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Lateral Flow ◽  
Reference Standards ◽  
Superior Performance ◽  
Sensitivity Analyses ◽  
Diagnostic Accuracy Study ◽  
Serum Samples ◽  
Finger Prick ◽  
Accuracy Study

Abstract Objective To evaluate the performance of new lateral flow immunoassays (LFIAs) suitable for use in a national coronavirus disease 2019 (covid-19) seroprevalence programme (real time assessment of community transmission 2—React 2). Design Diagnostic accuracy study. Setting Laboratory analyses were performed in the United Kingdom at Imperial College, London and university facilities in London. Research clinics for finger prick sampling were run in two affiliated NHS trusts. Participants Sensitivity analyses were performed on sera stored from 320 previous participants in the React 2 programme with confirmed previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Specificity analyses were performed on 1000 prepandemic serum samples. 100 new participants with confirmed previous SARS-CoV-2 infection attended study clinics for finger prick testing. Interventions Laboratory sensitivity and specificity analyses were performed for seven LFIAs on a minimum of 200 serum samples from participants with confirmed SARS-CoV-2 infection and 500 prepandemic serum samples, respectively. Three LFIAs were found to have a laboratory sensitivity superior to the finger prick sensitivity of the LFIA currently used in React 2 seroprevalence studies (84%). These LFIAs were then further evaluated through finger prick testing on participants with confirmed previous SARS-CoV-2 infection: two LFIAs (Surescreen, Panbio) were evaluated in clinics in June-July 2020 and the third LFIA (AbC-19) in September 2020. A spike protein enzyme linked immunoassay and hybrid double antigen binding assay were used as laboratory reference standards. Main outcome measures The accuracy of LFIAs in detecting immunoglobulin G (IgG) antibodies to SARS-CoV-2 compared with two reference standards. Results The sensitivity and specificity of seven new LFIAs that were analysed using sera varied from 69% to 100%, and from 98.6% to 100%, respectively (compared with the two reference standards). Sensitivity on finger prick testing was 77% (95% confidence interval 61.4% to 88.2%) for Panbio, 86% (72.7% to 94.8%) for Surescreen, and 69% (53.8% to 81.3%) for AbC-19 compared with the reference standards. Sensitivity for sera from matched clinical samples performed on AbC-19 was significantly higher with serum than finger prick at 92% (80.0% to 97.7%, P=0.01). Antibody titres varied considerably among cohorts. The numbers of positive samples identified by finger prick in the lowest antibody titre quarter varied among LFIAs. Conclusions One new LFIA was identified with clinical performance suitable for potential inclusion in seroprevalence studies. However, none of the LFIAs tested had clearly superior performance to the LFIA currently used in React 2 seroprevalence surveys, and none showed sufficient sensitivity and specificity to be considered for routine clinical use.

scholarly journals Diagnostic accuracy of a novel lateral-flow device in invasive aspergillosis: a meta-analysis

2015 ◽  
Vol 64 (7) ◽  
pp. 702-707 ◽  
Author(s):  
Zhijie Pan ◽  
Mengjiao Fu ◽  
Jiaojiao Zhang ◽  
Hua Zhou ◽  
Yiqi Fu ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Invasive Aspergillosis ◽  
Meta Analysis ◽  
Lateral Flow ◽  
Lateral Flow Device ◽  
Flow Device

scholarly journals Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow assay for antibody prevalence studies following vaccination: a diagnostic accuracy study

2021 ◽  
Vol 6 ◽  
pp. 358
Author(s):  
Alexandra Cann ◽  
Candice Clarke ◽  
Jonathan Brown ◽  
Tina Thomson ◽  
Maria Prendecki ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Diagnostic Accuracy ◽  
Natural Infection ◽  
Protein Detection ◽  
Lateral Flow ◽  
Antibody Responses ◽  
Spike Protein ◽  
Diagnostic Accuracy Study ◽  
Extensive Evaluation ◽  
Accuracy Study

Background: Lateral flow immunoassays (LFIAs) are able to achieve affordable, large scale antibody testing and provide rapid results without the support of central laboratories. As part of the development of the REACT programme extensive evaluation of LFIA performance was undertaken with individuals following natural infection. Here we assess the performance of the selected LFIA to detect antibody responses in individuals who have received at least one dose of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine. Methods: This was a prospective diagnostic accuracy study. Sampling was carried out at renal outpatient clinic and healthcare worker testing sites at Imperial College London NHS Trust. Two cohorts of patients were recruited; the first was a cohort of 108 renal transplant patients attending clinic following two doses of SARS-CoV-2 vaccine, the second cohort comprised 40 healthcare workers attending for first SARS-CoV-2 vaccination and subsequent follow up. During the participants visit, finger-prick blood samples were analysed on LFIA device, while paired venous sampling was sent for serological assessment of antibodies to the spike protein (anti-S) antibodies. Anti-S IgG was detected using the Abbott Architect SARS-CoV-2 IgG Quant II CMIA. A total of 186 paired samples were collected. The accuracy of Fortress LFIA in detecting IgG antibodies to SARS-CoV-2 compared to anti-spike protein detection on Abbott Assay Results: The LFIA had an estimated sensitivity of 92.0% (114/124; 95% confidence interval [CI] 85.7% to 96.1%) and specificity of 93.6% (58/62; 95% CI 84.3% to 98.2%) using the Abbott assay as reference standard (using the threshold for positivity of 7.10 BAU/ml) Conclusions: Fortress LFIA performs well in the detection of antibody responses for intended purpose of population level surveillance but does not meet criteria for individual testing.

scholarly journals SARS-CoV-2 Antibody Lateral Flow Assay for antibody prevalence studies following vaccine roll out: a Diagnostic Accuracy Study

2021 ◽  
Author(s):  
Alexandra H C Cann ◽  
Candice L Clarke ◽  
Jonathan C Brown ◽  
Tina Thomson ◽  
Maria Prendecki ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Natural Infection ◽  
Protein Detection ◽  
Imperial College ◽  
Lateral Flow ◽  
Antibody Responses ◽  
Spike Protein ◽  
Diagnostic Accuracy Study ◽  
Extensive Evaluation ◽  
Accuracy Study

Background Lateral flow immunoassays (LFIAs) have the potential to deliver affordable, large scale antibody testing and provide rapid results without the support of central laboratories. As part of the development of the REACT programme extensive evaluation of LFIA performance was undertaken with individuals following natural infection. Here we assess the performance of the selected LFIA to detect antibody responses in individuals who have received at least one dose of SARS-CoV-2 vaccine. Methods This is a prospective diagnostic accuracy study. Setting Sampling was carried out at renal outpatient clinic and healthcare worker testing sites at Imperial College London NHS Trust. Laboratory analyses were performed across Imperial College London sites and university facilities. Participants Two cohorts of patients were recruited; the first was a cohort of 108 renal transplant patients attending clinic following SARS-CoV-2 vaccine booster, the second cohort comprised 40 healthcare workers attending for first SARS-CoV-2 vaccination, and 21 day follow up. A total of 186 paired samples were collected. Interventions During the participants visit, capillary blood samples were analysed on LFIA device, while paired venous sampling was sent for serological assessment of antibodies to the spike protein (anti-S) antibodies. Anti-S IgG were detected using the Abbott Architect SARS-CoV-2 IgG Quant II CMIA. Main outcome measures The accuracy of Fortress LFIA in detecting IgG antibodies to SARS-CoV-2 compared to anti-spike protein detection on Abbott Assay. Results Using the threshold value for positivity on serological testing of ≥7.10 BAU/ml, the overall performance of the test produces an estimate of sensitivity of 91.94% (95% CI 85.67% to 96.06%) and specificity of 93.55% (95% CI 84.30% to 98.21%) using the Abbott assay as reference standard. Conclusions Fortress LFIA performs well in the detection of antibody responses for intended purpose of population level surveys, but does not meet criteria for individual testing.

Machine Learning Versus Standard Techniques for Updating Searches for Systematic Reviews: A Diagnostic Accuracy Study

2017 ◽  
Vol 167 (3) ◽  
pp. 213 ◽  
Author(s):  
Paul G. Shekelle ◽  
Kanaka Shetty ◽  
Sydne Newberry ◽  
Margaret Maglione ◽  
Aneesa Motala
Keyword(s):  
Machine Learning ◽  
Diagnostic Accuracy ◽  
Systematic Reviews ◽  
Diagnostic Accuracy Study ◽  
Accuracy Study ◽  
Standard Techniques
Sign in / Sign up

Export Citation Format

Share Document