Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay

The lateral flow assay (LFA) has been considered a rapid test tool but with low sensitivity hampering the precise diagnosis. Here, the authors report bioengineered enrichment tools for LFAs with enhanced sensitivity and specificity that can reinforce LFA's clinical performance. 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 (SMART(AI)-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and 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 SMART(AI)-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMART(AI)-LFA. We envision a smartphone-based SMART(AI)-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.

Automated summary

The authors present bioengineered enrichment tools for LFAs with enhanced sensitivity and specificity, which can improve the accuracy of LFA diagnosis. They also introduce a smartphone-based LFA diagnostics using deep learning-assisted algorithms, achieving high accuracy and reliability.