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28 September 2023 Deep-learning enabled point-of-care sensor for Lyme disease diagnosis
Hyou-Arm Joung, Rajesh Ghosh, Artem Goncharov, Barath Palanisamy, Kevin Ngo, Katarina Pejcinovic, Nicole Krockenberger, Elizabeth J. Horn, Raymond J. Dattwyler, Paul M. Arnaboldi, Omai Garner, Dino Di Carlo, Aydogan Ozcan
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265505 (2023) https://doi.org/10.1117/12.2678215
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
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.
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Hyou-Arm Joung, Rajesh Ghosh, Artem Goncharov, Barath Palanisamy, Kevin Ngo, Katarina Pejcinovic, Nicole Krockenberger, Elizabeth J. Horn, Raymond J. Dattwyler, Paul M. Arnaboldi, Omai Garner, Dino Di Carlo, and Aydogan Ozcan "Deep-learning enabled point-of-care sensor for Lyme disease diagnosis", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265505 (28 September 2023); https://doi.org/10.1117/12.2678215
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KEYWORDS
Diseases and disorders
Point-of-care devices
Diagnostics
Sensors
Antibodies
Detection and tracking algorithms
Medicine
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