Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 79 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an initial bacterial cell or less. This performance, compared to that of the gold standard mass spectrometry over a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

Geon Kim, Daewoong Ahn, Minhee Kang, Jinho Park, DongHun Ryu, YoungJu Jo, Jinyeop Song, Jea Sung Ryu, Gunho Choi, Hyun Jung Chung, Kyuseok Kim, Doo Ryeon Chung, In Young Yoo, Hee Jae Huh, Hyun-seok Min, Nam Yong Lee, YongKeun Park