Abstract
Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity.
Methods
Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues.
Results
Automated analysis showed substantial agreement with human experts (Cohen’s kappa 0.90–0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7–99.4%) and sensitivity (90.1–97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets).
Conclusions
Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.
Plain language summary
During the COVID-19 pandemic, antibody test kits, for use at home, were used to estimate how many people had COVID antibodies. These estimations indicated how many people have been exposed to the virus or have antibodies due to vaccination. However, some positive test results can be very faint, and be mistaken as negative. In our work, 500,000 people reported their antibody test results and submitted a photograph of their test. We designed a computerised system—a highly specialised artificial-intelligence (AI) system—that has high agreement with experts and can highlight potential mistakes by the public in reading the results of their home tests. This AI system makes it possible to improve the accuracy of monitoring COVID antibodies at the population level (e.g. whole country), which could inform decisions on public health, such as when booster vaccines should be administered.
| Original language | English |
|---|---|
| Article number | 78 |
| Journal | Communications Medicine |
| Volume | 2 |
| Early online date | 6 Jul 2022 |
| DOIs | |
| Publication status | Published - Dec 2022 |
Access to Document
- Wong_etal_CM_Machine_Learning_To_VOR
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