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.

由于许多侧流分析 (LFA) 每天都要进行测试，因此准确性的提高会极大地影响个体患者护理和公共卫生。然而，目前用于 COVID-19 检测的自测精度较低，这主要是由于 LFA 的灵敏度和读数歧义。在这里，我们展示了深度学习辅助的基于智能手机的 LFA (SMART ^AI -LFA) 诊断，以提供具有更高灵敏度的准确决策。结合临床数据学习和两步算法，可以通过临床数据的盲测进行无支架现场检测，其准确性高于未经训练的个人和人类专家（n = 1500）。我们在 135 项基于智能手机应用程序的临床测试中获得了 98% 的准确率，测试对象为不同的用户/智能手机。此外，通过更多的低滴度测试，我们观察到 SMART ^AI -LFA 的准确率保持在 99% 以上，而人类准确率却有显着下降，表明 SMART ^AI -LFA 的性能可靠。我们设想了一种基于智能手机的 SMART ^AI -LFA，它可以通过添加临床测试来不断提高性能，并满足数字化实时诊断的新标准。