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Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring



Yangyang Zhao, Boqun Dong, Kurt D. Benkstein, Lei Chen, Kristen L. Steffens, Stephen Semancik


Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO2 nanopillars covered with a 15 nm metal–organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5–80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.
ACS Applied Materials and Interfaces


plasmonic sensor, image analysis, neural networks, breath biomarker


Zhao, Y. , Dong, B. , Benkstein, K. , Chen, L. , Steffens, K. and Semancik, S. (2022), Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring, ACS Applied Materials and Interfaces, [online],, (Accessed June 19, 2024)


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Created November 23, 2022, Updated March 2, 2023