Presentation
16 March 2023 Rapid identification of individual bacterial pathogens using three-dimensional quantitative phase imaging and artificial neural network (Conference Presentation)
Author Affiliations +
Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC1238908 (2023) https://doi.org/10.1117/12.2654641
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
Rapid identification of infectious pathogens can save lives and mitigate healthcare expenses. Yet the current turnaround time for microbial identification typically exceeds 24 hours, as the common methods require the cultivation of millions or more bacteria to detect the collective signal. In this study, we propose a hybrid framework of quantitative phase imaging and artificial neural network to facilitate rapid identification at an individual-cell level. Specifically, three-dimensional images of refractive index were acquired for individual bacteria, and an optimized artificial neural network determined the species based on the three-dimensional morphologies, securing 82.5% blind test accuracy at an individual-cell level.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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, and YongKeun Park "Rapid identification of individual bacterial pathogens using three-dimensional quantitative phase imaging and artificial neural network (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC1238908 (16 March 2023); https://doi.org/10.1117/12.2654641
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KEYWORDS
Pathogens

3D image processing

3D metrology

Artificial neural networks

Phase imaging

Biomedical optics

Mass spectrometry

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