ArticleBMC digital health2026
Seeing beyond positivity: computer vision approach to decoding BinaxNOW COVID-19 tests and forecasting negative results.
Article in BMC digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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13 authors.
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Abstract
Background: The emergence of COVID-19 facilitated the widespread use, regulatory awareness, and societal familiarity of lateral flow assays (LFA) for self-diagnosis. The increased frequency in the use of LFA's as well as their increased use by the general population amplified some of the major drawbacks of these tests, namely, the reliance on user interpretation of the test result, the qualitative nature of the result itself, and the lower sensitivity and specificity compared to traditional polymerase chain reaction (PCR)-based diagnostic testing. Furthermore, the results of these tests and the interpretation of them by lay users were increasingly used as a public health tool by authorities, where recommendations were made on how members of the public should act in response to self-interpretation of test results. Our aims were to develop an objective image analysis algorithm that can (1) interpret the result of a popular COVID-19 LFA Rapid Antigen Test (RAT) and accurately estimate viral load, and (2) predict how long after a positive test a user will test negative on the RAT, indicating loss of infectiousness and the ability to be around others without fear of spreading infection. Results: Indeed, we found that we could estimate viral load to within a RMSE of 1.44 Cycle Threshold (C Conclusion: The development of an objective image analysis algorithm for estimating viral load combined with smartphone apps represents a significant improvement upon LFA RAT technologies being used by millions worldwide to diagnose a variety of epidemiologically significant diseases. Supplementary Information: The online version contains supplementary material available at 10.1186/s44247-026-00287-4.
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