Deep learning-based point-of-care diagnostic test for Lyme disease

We report a point-of-care (POC) assay and neural network-based diagnostic algorithm for Lyme Disease (LD). A paper-based test in a vertical flow format detects 16 different IgM and IgG LD-specific antibodies in serum using a mobile phone reader and automated image processing to quantify its colorimetric signals. The multiplexed information is then input into a trained neural-network which infers a positive or negative result for LD. The assay and diagnostic decision algorithm were validated through fully-blinded testing of human serum samples yielding an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0% respectively, outperforming previous Lyme POC tests.