ArticleJMIR public health and surveillance2022
A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays.
Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Transport-Reaction-Signal Coupling in Lateral Flow Assays for Next-Generation Point-of-Care Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Multi-Provincial Clinical Evaluation of the PANBIO™ COVID-19 Antigen Rapid Test Device in South Africa.Diagnostics (Basel, Switzerland) · 2026Article
- The evolution of next-generation lateral flow assays for bacterial and fungal diagnostics.Mikrochimica acta · 2026Review
- Smartphones as Catalysts for Synergistic Nutrition: A New Era in Bioactive Detection, Personalization, and Food System Intelligence.Food science & nutrition · 2025Review
- AI-enhanced rapid diagnostic testing platform for mass opisthorchiasis screening.Scientific reports · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs).Scientific reports · 2024Article
- Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results.Frontiers in public health · 2024Article
- Digital Platform for Automatic Qualitative and Quantitative Reading of a Cryptococcal Antigen Point-of-Care Assay Leveraging Smartphones and Artificial Intelligence.Journal of fungi (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, altering a correct epidemiological surveillance.
objectiveOur 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.
methodsOverall, 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.
resultsField 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.
conclusionsThe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.