Rapid Identification of Infectious Pathogens at the Single-Cell Level via Combining Hyperspectral Microscopic Images and Deep Learning

Abstract

Identifying infectious pathogens quickly and accurately is significant for patients and doctors. Identifying single bacterial strains is significant in eliminating culture and speeding up diagnosis. We present an advanced optical method for the rapid detection of infectious (including common and uncommon) pathogens by combining hyperspectral microscopic imaging and deep learning. To acquire more information regarding the pathogens, we developed a hyperspectral microscopic imaging system with a wide wavelength range and fine spectral resolution. Furthermore, an end-to-end deep learning network based on feature fusion, called BI-Net, was designed to extract the species-dependent features encoded in cell-level hyperspectral images as the fingerprints for species differentiation. After being trained based on a large-scale dataset that we built to identify common pathogens, BI-Net was used to classify uncommon pathogens via transfer learning. An extensive analysis demonstrated that BI-Net was able to learn species-dependent characteristics, with the classification accuracy and Kappa coefficients being 92% and 0.92, respectively, for both common and uncommon species. Our method outperformed state-of-the-art methods by a large margin and its excellent performance demonstrates its excellent potential in clinical practice.