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