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Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.

David-A Mendels1, Laurent Dortet2,3,4, Cécile Emeraud5,3,4

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Proceedings of the National Academy of Sciences of the United States of America
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A new smartphone app uses machine learning to interpret COVID-19 rapid diagnostic tests (RDTs), significantly improving accuracy and reducing subjective interpretation of results for clinicians and patients.

Keywords:
SARS-CoV-2machine learningsmartphone application

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Area of Science:

  • Medical Diagnostics
  • Machine Learning Applications
  • Infectious Disease Testing

Background:

  • Serological rapid diagnostic tests (RDTs) are crucial for quick disease detection, offering binary results within minutes.
  • The COVID-19 pandemic saw a surge in SARS-CoV-2 RDTs, but visual interpretation of results can be subjective and vary.
  • Ambiguities in reading RDTs can lead to diagnostic uncertainty among healthcare professionals and potential for patient self-testing errors.

Purpose of the Study:

  • To develop and assess the accuracy of a smartphone application, xRCovid, for objective classification of SARS-CoV-2 serological RDT results.
  • To reduce reading ambiguities associated with visual interpretation of RDTs.
  • To enhance confidence in RDT usage for clinicians, laboratory staff, and potentially for patient self-testing.

Main Methods:

  • Development of a machine learning-based smartphone application (xRCovid) designed to classify results from SARS-CoV-2 serological RDTs.
  • Evaluation of the app's accuracy and performance across 11 different COVID-19 RDT models.
  • Comparison of the app's classification results against traditional visual interpretation methods.

Main Results:

  • The xRCovid application achieved a precision of 99.3% in classifying SARS-CoV-2 RDT results when compared to visual interpretation.
  • The machine learning approach significantly reduced the subjectivity inherent in reading visible color bands on RDTs.
  • The app demonstrated high accuracy across multiple RDT models, indicating broad applicability.

Conclusions:

  • The smartphone application effectively replaces subjective visual RDT interpretation with a more objective, lower-uncertainty image classification.
  • Utilizing the xRCovid app can increase confidence for healthcare professionals and laboratory staff when performing RDTs.
  • This technology opens new avenues for reliable patient self-testing of COVID-19, improving accessibility to diagnostics.