Lyme disease, the most common tick-borne illness in the United States, is a significant clinical diagnostic challenge. The current approach is standard or modified two-tiered tests (STTT/MTTT), which both offer limited accuracy. A biosensing platform with nanoscale plasmonic gratings to enhance signal from antibodies was developed to resolve these issues. Human serum samples with known Lyme disease status were tested on the biosensor. These samples included patients in early and late stages of the disease, as well as healthy controls. The results were analyzed with ROC analysis to determine diagnostic thresholds. Scoring the data with these thresholds showed improved accuracy over STTT and MTTT. Further machine learning analysis on the data yielded similar results to the ROC analysis.. Based on these results, this biosensor has the potential to improve clinical outcomes for Lyme disease by enabling earlier disease diagnosis through higher sensitivity toward early stages of Lyme disease.
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