Abstract Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health.However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities.Here, we present deep learning-assisted smartphone-based LFA (SMARTAI-LFA) diagnostics to provide accurate decisions with higher sensitivity.Combining clinical data learning and syil x5 price two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500).
We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones.Furthermore, with more low-titer tests, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA.We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced sonics empire performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.