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Liu G, Wang J, Wang J, Cui X, Wang K, Chen M, Yang Z, Gao A, Shen Y, Zhang Q, Gao G, Cui D. Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers. Talanta 2024; 273:125868. [PMID: 38458085 DOI: 10.1016/j.talanta.2024.125868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 03/10/2024]
Abstract
Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this study, we innovatively developed zwitterionic silica-coated MNPs (MNP@Si-Zwit/COOH) with outstanding antifouling capabilities and effectively utilised them for the simultaneous identification of the nucleocapsid protein (N protein) of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and influenza A/B. The carboxyl-functionalised MNPs with 10% zwitterionic ligands (MNP@Si-Zwit 10/COOH) exhibited a wide linear dynamic detection range and the most pronounced signal-to-noise ratio when used as probes in the ICT. The relative limit of detection (LOD) values were achieved in 12 min by using a magnetic assay reader (MAR), with values of 0.0062 ng/mL for SARS-CoV-2 and 0.0051 and 0.0147 ng/mL, respectively, for the N protein of influenza A and influenza B. By integrating computer vision and deep learning to enhance the image processing of immunoassay results for multiplex detection, a classification accuracy in the range of 0.9672-0.9936 was achieved for evaluating the three proteins at concentrations of 0, 0.1, 1, and 10 ng/mL. The proposed MNP-based ICT for the multiplex diagnosis of biomarkers holds substantial promise for applications in both medical institutions and self-administered diagnostic settings.
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Affiliation(s)
- Guan Liu
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Junhao Wang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Jiulin Wang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Xinyuan Cui
- Radiology Department of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai, 200025, PR China
| | - Kan Wang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Mingrui Chen
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Ziyang Yang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Ang Gao
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Yulan Shen
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, PR China.
| | - Qian Zhang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
| | - Guo Gao
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
| | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China; National Engineering Research Center for Nanotechnology, Shanghai, 200241, PR China; Henan Medical School, Henan University, Henan, 475004, PR China.
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Song X, Cao Y, Yan F. Isothermal Nucleic Acid Amplification-Based Lateral Flow Testing for the Detection of Plant Viruses. Int J Mol Sci 2024; 25:4237. [PMID: 38673821 PMCID: PMC11050433 DOI: 10.3390/ijms25084237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Isothermal nucleic acid amplification-based lateral flow testing (INAA-LFT) has emerged as a robust technique for on-site pathogen detection, providing a visible indication of pathogen nucleic acid amplification that rivals or even surpasses the sensitivity of real-time quantitative PCR. The isothermal nature of INAA-LFT ensures consistent conditions for nucleic acid amplification, establishing it as a crucial technology for rapid on-site pathogen detection. However, despite its considerable promise, the widespread application of isothermal INAA amplification-based lateral flow testing faces several challenges. This review provides an overview of the INAA-LFT procedure, highlighting its advancements in detecting plant viruses. Moreover, the review underscores the imperative of addressing the existing limitations and emphasizes ongoing research efforts dedicated to enhancing the applicability and performance of this technology in the realm of rapid on-site testing.
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Affiliation(s)
- Xuemei Song
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo 315211, China;
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Plant Virology, Ningbo University, Ningbo 315211, China;
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo 315211, China
| | - Yuhao Cao
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo 315211, China;
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Plant Virology, Ningbo University, Ningbo 315211, China;
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo 315211, China
| | - Fei Yan
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Plant Virology, Ningbo University, Ningbo 315211, China;
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo 315211, China
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Roche SD, Ekwunife OI, Mendonca R, Kwach B, Omollo V, Zhang S, Ongwen P, Hattery D, Smedinghoff S, Morris S, Were D, Rech D, Bukusi EA, Ortblad KF. Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results. Front Public Health 2024; 12:1334881. [PMID: 38384878 PMCID: PMC10880864 DOI: 10.3389/fpubh.2024.1334881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction HIV self-testing (HIVST) is highly sensitive and specific, addresses known barriers to HIV testing (such as stigma), and is recommended by the World Health Organization as a testing option for the delivery of HIV pre-exposure prophylaxis (PrEP). Nevertheless, HIVST remains underutilized as a diagnostic tool in community-based, differentiated HIV service delivery models, possibly due to concerns about result misinterpretation, which could lead to inadvertent onward transmission of HIV, delays in antiretroviral therapy (ART) initiation, and incorrect initiation on PrEP. Ensuring that HIVST results are accurately interpreted for correct clinical decisions will be critical to maximizing HIVST's potential. Early evidence from a few small pilot studies suggests that artificial intelligence (AI) computer vision and machine learning could potentially assist with this task. As part of a broader study that task-shifted HIV testing to a new setting and cadre of healthcare provider (pharmaceutical technologists at private pharmacies) in Kenya, we sought to understand how well AI technology performed at interpreting HIVST results. Methods At 20 private pharmacies in Kisumu, Kenya, we offered free blood-based HIVST to clients ≥18 years purchasing products indicative of sexual activity (e.g., condoms). Trained pharmacy providers assisted clients with HIVST (as needed), photographed the completed HIVST, and uploaded the photo to a web-based platform. In real time, each self-test was interpreted independently by the (1) client and (2) pharmacy provider, with the HIVST images subsequently interpreted by (3) an AI algorithm (trained on lab-captured images of HIVST results) and (4) an expert panel of three HIVST readers. Using the expert panel's determination as the ground truth, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HIVST result interpretation for the AI algorithm as well as for pharmacy clients and providers, for comparison. Results From March to June 2022, we screened 1,691 pharmacy clients and enrolled 1,500 in the study. All clients completed HIVST. Among 854 clients whose HIVST images were of sufficient quality to be interpretable by the AI algorithm, 63% (540/854) were female, median age was 26 years (interquartile range: 22-31), and 39% (335/855) reported casual sexual partners. The expert panel identified 94.9% (808/854) of HIVST images as HIV-negative, 5.1% (44/854) as HIV-positive, and 0.2% (2/854) as indeterminant. The AI algorithm demonstrated perfect sensitivity (100%), perfect NPV (100%), and 98.8% specificity, and 81.5% PPV (81.5%) due to seven false-positive results. By comparison, pharmacy clients and providers demonstrated lower sensitivity (93.2% and 97.7% respectively) and NPV (99.6% and 99.9% respectively) but perfect specificity (100%) and perfect PPV (100%). Conclusions AI computer vision technology shows promise as a tool for providing additional quality assurance of HIV testing, particularly for catching Type II error (false-negative test interpretations) committed by human end-users. We discuss possible use cases for this technology to support differentiated HIV service delivery and identify areas for future research that is needed to assess the potential impacts-both positive and negative-of deploying this technology in real-world HIV service delivery settings.
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Affiliation(s)
- Stephanie D. Roche
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Obinna I. Ekwunife
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | | | - Benn Kwach
- Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Victor Omollo
- Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Shengruo Zhang
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | | | | | | | | | | | | | - Elizabeth A. Bukusi
- Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya
- Department of Global Health, University of Washington, Seattle, WA, United States
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, United States
| | - Katrina F. Ortblad
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
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Pannipulath Venugopal V, Babu Saheer L, Maktabdar Oghaz M. COVID-19 lateral flow test image classification using deep CNN and StyleGAN2. Front Artif Intell 2024; 6:1235204. [PMID: 38348096 PMCID: PMC10860423 DOI: 10.3389/frai.2023.1235204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Introduction Artificial intelligence (AI) in healthcare can enhance clinical workflows and diagnoses, particularly in large-scale operations like COVID-19 mass testing. This study presents a deep Convolutional Neural Network (CNN) model for automated COVID-19 RATD image classification. Methods To address the absence of a RATD image dataset, we crowdsourced 900 real-world images focusing on positive and negative cases. Rigorous data augmentation and StyleGAN2-ADA generated simulated images to overcome dataset limitations and class imbalances. Results The best CNN model achieved a 93% validation accuracy. Test accuracies were 88% for simulated datasets and 82% for real datasets. Augmenting simulated images during training did not significantly improve real-world test image performance but enhanced simulated test image performance. Discussion The findings of this study highlight the potential of the developed model in expediting COVID-19 testing processes and facilitating large-scale testing and tracking systems. The study also underscores the challenges in designing and developing such models, emphasizing the importance of addressing dataset limitations and class imbalances. Conclusion This research contributes to the deployment of large-scale testing and tracking systems, offering insights into the potential applications of AI in mitigating outbreaks similar to COVID-19. Future work could focus on refining the model and exploring its adaptability to other healthcare scenarios.
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Affiliation(s)
| | - Lakshmi Babu Saheer
- School of Computing and Information Science, Anglia Ruskin University, Cambridge, United Kingdom
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Ward H, Atchison C, Whitaker M, Davies B, Ashby D, Darzi A, Chadeau-Hyam M, Riley S, Donnelly CA, Barclay W, Cooke GS, Elliott P. Design and Implementation of a National Program to Monitor the Prevalence of SARS-CoV-2 IgG Antibodies in England Using Self-Testing: The REACT-2 Study. Am J Public Health 2023; 113:1201-1209. [PMID: 37733993 PMCID: PMC10568505 DOI: 10.2105/ajph.2023.307381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2023] [Indexed: 09/23/2023]
Abstract
Data System. The UK Department of Health and Social Care funded the REal-time Assessment of Community Transmission-2 (REACT-2) study to estimate community prevalence of SARS-CoV-2 IgG (immunoglobulin G) antibodies in England. Data Collection/Processing. We obtained random cross-sectional samples of adults from the National Health Service (NHS) patient list (near-universal coverage). We sent participants a lateral flow immunoassay (LFIA) self-test, and they reported the result online. Overall, 905 991 tests were performed (28.9% response) over 6 rounds of data collection (June 2020-May 2021). Data Analysis/Dissemination. We produced weighted estimates of LFIA test positivity (validated against neutralizing antibodies), adjusted for test performance, at local, regional, and national levels and by age, sex, and ethnic group and area-level deprivation score. In each round, fieldwork occurred over 2 weeks, with results reported to policymakers the following week. We disseminated results as preprints and peer-reviewed journal publications. Public Health Implications. REACT-2 estimated the scale and variation in antibody prevalence over time. Community self-testing and -reporting produced rapid insights into the changing course of the pandemic and the impact of vaccine rollout, with implications for future surveillance. (Am J Public Health. 2023;113(11):1201-1209. https://doi.org/10.2105/AJPH.2023.307381).
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Affiliation(s)
- Helen Ward
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Christina Atchison
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Matthew Whitaker
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Bethan Davies
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Deborah Ashby
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Ara Darzi
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Marc Chadeau-Hyam
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Steven Riley
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Christl A Donnelly
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Wendy Barclay
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Graham S Cooke
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Paul Elliott
- Helen Ward, Christina Atchison, Matthew Whitaker, Bethan Davies, Deborah Ashby, Marc Chadeau-Hyam, Steven Riley, and Paul Elliott are with the School of Public Health, Imperial College London, UK. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Christl A. Donnelly is with the Department of Statistics, University of Oxford, Oxford, UK. Wendy Barclay and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
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Omidfar K, Riahi F, Kashanian S. Lateral Flow Assay: A Summary of Recent Progress for Improving Assay Performance. BIOSENSORS 2023; 13:837. [PMID: 37754072 PMCID: PMC10526804 DOI: 10.3390/bios13090837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023]
Abstract
Lateral flow tests are one of the most important types of paper-based point-of-care (POCT) diagnostic tools. It shows great potential as an implement for improving the rapid screening and management of infections in global pandemics or other potential health disorders by using minimally expert staff in locations where no sophisticated laboratory services are accessible. They can detect different types of biomarkers in various biological samples and provide the results in a little time at a low price. An important challenge regarding conventional LFAs is increasing their sensitivity and specificity. There are two main approaches to increase sensitivity and specificity, including assay improvement and target enrichment. Assay improvement comprises the assay optimization and signal amplification techniques. In this study, a summarize of various sensitivity and specificity enhancement strategies with an objective evaluation are presented, such as detection element immobilization, capillary flow rate adjusting, label evolution, sample extraction and enrichment, etc. and also the key findings in improving the LFA performance and solving their limitations are discussed along with numerous examples.
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Affiliation(s)
- Kobra Omidfar
- Biosensor Research Center, Endocrinology and Metabolism Molecular—Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran 1458889694, Iran
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran 1458889694, Iran
| | - Fatemeh Riahi
- Biosensor Research Center, Endocrinology and Metabolism Molecular—Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran 1458889694, Iran
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran 1458889694, Iran
| | - Soheila Kashanian
- Faculty of Chemistry, Razi University, Kermanshah 6714414971, Iran
- Nanobiotechnology Department, Faculty of Innovative Science and Technology, Razi University, Kermanshah 6714414971, Iran
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Whitaker M, Davies B, Atchison C, Barclay W, Ashby D, Darzi A, Riley S, Cooke G, Donnelly CA, Chadeau-Hyam M, Elliott P, Ward H. SARS-CoV-2 rapid antibody test results and subsequent risk of hospitalisation and death in 361,801 people. Nat Commun 2023; 14:4957. [PMID: 37587102 PMCID: PMC10432566 DOI: 10.1038/s41467-023-40643-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
The value of SARS-CoV-2 lateral flow immunoassay (LFIA) tests for estimating individual disease risk is unclear. The REACT-2 study in England, UK, obtained self-administered SARS-CoV-2 LFIA test results from 361,801 adults in January-May 2021. Here, we link to routine data on subsequent hospitalisation (to September 2021), and death (to December 2021). Among those who had received one or more vaccines, a negative LFIA is associated with increased risk of hospitalisation with COVID-19 (HR: 2.73 [95% confidence interval: 1.15,6.48]), death (all-cause) (HR: 1.59, 95% CI:1.07, 2.37), and death with COVID-19 as underlying cause (20.6 [1.83,232]). For people designated at high risk from COVID-19, who had received one or more vaccines, there is an additional risk of all-cause mortality of 1.9 per 1000 for those testing antibody negative compared to positive. However, the LFIA does not provide substantial predictive information over and above that which is available from detailed sociodemographic and health-related variables. Nonetheless, this simple test provides a marker which could be a valuable addition to understanding population and individual-level risk.
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Affiliation(s)
- Matthew Whitaker
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Bethan Davies
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Christina Atchison
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Wendy Barclay
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Graham Cooke
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Marc Chadeau-Hyam
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, London, UK.
- MRC Centre for Environment and Health, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK.
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK.
- Health Data Research (HDR) UK London at Imperial College, London, UK.
- UK Dementia Research Institute at Imperial College, London, UK.
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
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Arumugam S, Ma J, Macar U, Han G, McAulay K, Ingram D, Ying A, Chellani HH, Chern T, Reilly K, Colburn DAM, Stanciu R, Duffy C, Williams A, Grys T, Chang SF, Sia SK. Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics. COMMUNICATIONS MEDICINE 2023; 3:91. [PMID: 37353603 DOI: 10.1038/s43856-023-00312-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/01/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format. METHODS We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images. RESULTS Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images. CONCLUSIONS The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.
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Affiliation(s)
- Siddarth Arumugam
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jiawei Ma
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Uzay Macar
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Guangxing Han
- Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA
| | - Kathrine McAulay
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | | | - Alex Ying
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | | | - Terry Chern
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Kenta Reilly
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - David A M Colburn
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Robert Stanciu
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Craig Duffy
- Safe Health Systems, Inc., Los Angeles, CA, 90036, USA
| | | | - Thomas Grys
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Shih-Fu Chang
- Department of Computer Science, Columbia University, New York, NY, 10027, USA.
- Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA.
| | - Samuel K Sia
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
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9
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Atchison CJ, Moshe M, Brown JC, Whitaker M, Wong NCK, Bharath AA, McKendry RA, Darzi A, Ashby D, Donnelly CA, Riley S, Elliott P, Barclay WS, Cooke GS, Ward H. Validity of Self-testing at Home With Rapid Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Detection by Lateral Flow Immunoassay. Clin Infect Dis 2023; 76:658-666. [PMID: 35913410 PMCID: PMC9384551 DOI: 10.1093/cid/ciac629] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/14/2022] [Accepted: 07/28/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based electrochemiluminescence immunoassay (ECLIA) and live virus neutralization. METHODS In July 2021, 3758 participants performed, at home, a self-administered Fortress LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of immunoglobulin G (IgG) antibodies using the Roche Elecsys® Anti-SARS-CoV-2 ECLIA. We compared the self-reported LFIA result to the quantitative ECLIA and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralization. RESULTS Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on ECLIA (using the manufacturer reference standard threshold for positivity of 0.8 U mL-1). Live virus neutralization was detected in 169 of 250 randomly selected samples (67.6%); 133/169 were positive with self-reported LFIA (sensitivity 78.7%; 95% confidence interval [CI]: 71.8, 84.6), 142/155 (91.6%; 95% CI: 86.1, 95.5) with ALFA, and 169 (100%; 95% CI: 97.8, 100.0) with ECLIA. There were 81 samples with no detectable virus neutralization; 47/81 were negative with self-reported LFIA (specificity 58.0%; 95% CI: 46.5, 68.9), 34/75 (45.3%; 95% CI: 33.8, 57.3) with ALFA, and 0/81 (0%; 95% CI: 0, 4.5) with ECLIA. CONCLUSIONS Self-administered LFIA is less sensitive than a quantitative antibody test, but the positivity in LFIA correlates better than the quantitative ECLIA with virus neutralization.
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Affiliation(s)
- Christina J Atchison
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Maya Moshe
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Jonathan C Brown
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Matthew Whitaker
- School of Public Health, Imperial College London, London, United Kingdom
| | - Nathan C K Wong
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Anil A Bharath
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Rachel A McKendry
- London Centre for Nanotechnology & Division of Medicine, University College London, London, United Kingdom
- Division of Medicine, University College London, London, United Kingdom
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Institute of Global Health Innovation at Imperial College London, London, United Kingdom
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Health Data Research (HDR) UK London at Imperial College, London, United Kingdom
- UK Dementia Research Institute at Imperial College, London, United Kingdom
| | - Wendy S Barclay
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Graham S Cooke
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Infectious Disease, Imperial College London, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
| | - Helen Ward
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
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10
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Digital Platform for Automatic Qualitative and Quantitative Reading of a Cryptococcal Antigen Point-of-Care Assay Leveraging Smartphones and Artificial Intelligence. J Fungi (Basel) 2023; 9:jof9020217. [PMID: 36836331 PMCID: PMC9961444 DOI: 10.3390/jof9020217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/19/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023] Open
Abstract
Cryptococcosis is a fungal infection that causes serious illness, particularly in immunocompromised individuals such as people living with HIV. Point of care tests (POCT) can help identify and diagnose patients with several advantages including rapid results and ease of use. The cryptococcal antigen (CrAg) lateral flow assay (LFA) has demonstrated excellent performance in diagnosing cryptococcosis, and it is particularly useful in resource-limited settings where laboratory-based tests may not be readily available. The use of artificial intelligence (AI) for the interpretation of rapid diagnostic tests can improve the accuracy and speed of test results, as well as reduce the cost and workload of healthcare professionals, reducing subjectivity associated with its interpretation. In this work, we analyze a smartphone-based digital system assisted by AI to automatically interpret CrAg LFA as well as to estimate the antigen concentration in the strip. The system showed excellent performance for predicting LFA qualitative interpretation with an area under the receiver operating characteristic curve of 0.997. On the other hand, its potential to predict antigen concentration based solely on a photograph of the LFA has also been demonstrated, finding a strong correlation between band intensity and antigen concentration, with a Pearson correlation coefficient of 0.953. The system, which is connected to a cloud web platform, allows for case identification, quality control, and real-time monitoring.
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11
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Sanchez T, Mavragani A, Álamo E, Pérez-Panizo N, Mousa A, Dacal E, Lin L, Vladimirov A, Cuadrado D, Mateos-Nozal J, Galán JC, Romero-Hernandez B, Cantón R, Luengo-Oroz M, Rodriguez-Dominguez M. A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays. JMIR Public Health Surveill 2022; 8:e38533. [PMID: 36265136 PMCID: PMC9840096 DOI: 10.2196/38533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/16/2022] [Accepted: 10/13/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, altering a correct epidemiological surveillance. OBJECTIVE Our aim was to evaluate an artificial intelligence-based smartphone app, connected to a cloud web platform, to automatically and objectively read RDT results and assess its impact on COVID-19 pandemic management. METHODS Overall, 252 human sera were used to inoculate a total of 1165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department. RESULTS Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8%-96.1%) for reading IgG band of COVID-19 antibody RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100%, and specificity was 95.8% (CI 94.3%-97.3%). All COVID-19 antigen RDTs were correctly read by the app. CONCLUSIONS The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDT brands. The web platform serves as a real-time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.
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Affiliation(s)
| | | | | | - Nuria Pérez-Panizo
- Servicio de Geriatría, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
| | | | | | - Lin Lin
- Spotlab, Madrid, Spain.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Jesús Mateos-Nozal
- Servicio de Geriatría, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
| | - Juan Carlos Galán
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Beatriz Romero-Hernandez
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Cantón
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Mario Rodriguez-Dominguez
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
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