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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Casado JL, Haemmerle J, Vizcarra P, Rodriguez-Dominguez M, Velasco T, Velasco H, Centenera E, Romero-Hernandez B, Fernandez-Escribano M, Vallejo A. T-cell response after first dose of BNT162b2 SARS-CoV-2 vaccine among healthcare workers with previous infection or cross-reactive immunity. Clin Transl Immunology 2021; 10:e1341. [PMID: 34522381 PMCID: PMC8426108 DOI: 10.1002/cti2.1341] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/22/2021] [Accepted: 08/21/2021] [Indexed: 12/28/2022] Open
Abstract
Objectives Antibody response to the first dose of BNT162b2 SARS‐CoV‐2 is greater in COVID‐19‐convalescent than in infection‐naïve individuals. However, there are no data about T‐cell response in individuals with pre‐existing cellular immunity. Methods We evaluated T‐cell responses in parallel with SARS‐CoV‐2 antibody level after first dose of BNT162b2 vaccine in 23 infection‐naïve and 27 convalescent healthcare workers (HCWs) previously included in a study about humoral and T‐cell immunity. Results Overall, the antibody response was lower in the infection‐naïve group than in convalescent individuals (18 895 vs 662.7 AU mL−1, P < 0.001), and intermediate but significantly lower in convalescent HCWs with previous negative serology (25 174 vs 1793 AU mL−1; P = 0.015). Indeed, anti‐spike IgG titres after the first dose correlated with baseline anti‐nucleocapsid IgG titres (rho = 0.689; P < 0.001). Pre‐existing T‐cell immunity was observed in 78% of convalescent and 65% of the infection‐naïve HCWs. T‐cell response after the first dose of the vaccine was observed in nearly all the cases with pre‐existing T‐cell immunity, reaching 94% in convalescent HCWs and 93% in those with cross‐reactive T cells. It was lower in the infection‐naïve group (50%; P = 0.087) and in convalescent HCWs with negative serology (56%; P = 0.085). Notably, systemic reactogenicity after vaccination was mainly observed in those with pre‐existing T‐cell immunity (P = 0.051). Conclusion Here, we report that the first dose of BTN162b2 elicits a similar S‐specific T‐cell response in cases of either past infection or cross‐reactive T cells, but lower in the rest of infection‐naïve individuals and in convalescent HCWs who have lost detectable specific antibodies during follow‐up.
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Affiliation(s)
- Jose L Casado
- Department of Infectious Diseases Hospital Universitario Ramón y Cajal Madrid Spain
| | - Johannes Haemmerle
- Department of Prevention of Occupational Risks Hospital Universitario Ramón y Cajal Madrid Spain
| | - Pilar Vizcarra
- Department of Infectious Diseases Hospital Universitario Ramón y Cajal Madrid Spain
| | - Mario Rodriguez-Dominguez
- Department of Microbiology IRYCIS, Instituto Ramón y Cajal de Investigaciones Sanitarias CIBERESP Hospital Universitario Ramón y Cajal Madrid Spain
| | - Tamara Velasco
- Department of Infectious Diseases Hospital Universitario Ramón y Cajal Madrid Spain
| | - Hector Velasco
- Laboratory of Immunovirology IRYCIS, Instituto Ramón y Cajal de Investigaciones Sanitarias CIBERESP Hospital Universitario Ramón y Cajal Madrid Spain
| | - Elena Centenera
- Department of Infectious Diseases Hospital Universitario Ramón y Cajal Madrid Spain
| | - Beatriz Romero-Hernandez
- Department of Microbiology IRYCIS, Instituto Ramón y Cajal de Investigaciones Sanitarias CIBERESP Hospital Universitario Ramón y Cajal Madrid Spain
| | | | - Alejandro Vallejo
- Department of Infectious Diseases Hospital Universitario Ramón y Cajal Madrid Spain.,Laboratory of Immunovirology IRYCIS, Instituto Ramón y Cajal de Investigaciones Sanitarias CIBERESP Hospital Universitario Ramón y Cajal Madrid Spain
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