Emerging nanophotonic platforms for infectious disease diagnostics: Re-imagining the conventional microbiology toolkit


Speaker(s): Dr. Jennifer A. Dionne
We present our research controlling light at the nanoscale for infectious disease diagnostics, including detecting bacteria at low concentration, sensing COVID antibodies and antigens, and visualizing in-vivo inter-cellular forces. First, we combine Raman spectroscopy and deep learning to accurately classify bacteria by both species and antibiotic resistance in a single step. We design a convolutional neural network (CNN) for spectral data and train it to identify 30 of the most common bacterial strains from single-cell Raman spectra, achieving antibiotic treatment identification accuracies exceeding 99% and species identification accuracies similar to leading mass spectrometry identification techniques. Our combined Raman-CNN system represents a proof-of-concept for rapid, culture-free identification of bacterial isolates and antibiotic resistance.

Pratt School of Engineering


Chemistry; Duke Materials Initiative; Electrical and Computer Engineering (ECE); Mechanical Engineering and Materials Science (MEMS); Physics; University Program in Materials Science and Engineering (MatSci)



Tyson, Quiana