Lyme disease (LD) is a tick-borne illness and can lead to severe health complications if left undiagnosed/untreated. This study introduces a novel approach to point-of-care testing for LD, utilizing deep-learning-enabled peptide-based serodiagnosis. 82 unique patient serum samples were used for validation, obtained from the Bay Area Lyme Disease Biobank and the CDC Lyme Serum Repository. Blinded test results demonstrated outstanding diagnostic performance, with 84.0% sensitivity and 96.3% specificity. By integrating deep learning techniques with a peptide-based sensing panel, this serodiagnosis offers a rapid (< 15 minutes) and cost-effective (< $0.5/test) platform for LD diagnostics, while minimizing cross-reactivity.
Lyme disease (LD) is a tick-borne illness caused by the bacterium Borrelia burgdorferi, which can cause severe symptoms if untreated. We present a novel diagnostic platform utilizing synthetic peptides and a deep-learning-based analytical algorithm to detect LD-specific antibodies in patient serum samples. Blinded samples acquired from the Centers for Disease Control and Prevention (CDC) were tested using our platform, achieving a sensitivity of 95% among disseminated disease and a specificity of 100% across all healthy endemic controls and cross-infected samples. Our peptide-based assay offers high sensitivity, specificity, ease-of-use, and cost-effectiveness, making it an attractive platform for point-of-care LD diagnosis.
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.
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