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20 August 2020 Deep learning-based point-of-care diagnostic test for Lyme disease
Zachary Scott Ballard, Hyouarm Joung, Jing Wu, Derek Tseng, Hailemariam Teshome, Linghao Zhao, Elizabeth J. Horn, Raymond Dattwyler, Paul M. Arnaboldi, Omai Garner, Dino DiCarlo, Aydogan Ozcan
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Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691I (2020) https://doi.org/10.1117/12.2567459
Event: SPIE Nanoscience + Engineering, 2020, Online Only
Abstract
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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Zachary Scott Ballard, Hyouarm Joung, Jing Wu, Derek Tseng, Hailemariam Teshome, Linghao Zhao, Elizabeth J. Horn, Raymond Dattwyler, Paul M. Arnaboldi, Omai Garner, Dino DiCarlo, and Aydogan Ozcan "Deep learning-based point-of-care diagnostic test for Lyme disease", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691I (20 August 2020); https://doi.org/10.1117/12.2567459
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KEYWORDS
Multiplexing
Diagnostic tests
Point-of-care devices
Evolutionary algorithms
Neural networks
Cell phones
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