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9 March 2020 Enhancing in vivo nose cancer detection with rapid fiberoptic Raman and deep learning techniques (Conference Presentation)
Chi Shu, Hanshu Yan, Kan Lin, Chwee Ming Lim, Wei Zheng, Jiashi Feng, Zhiwei Huang
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Proceedings Volume 11234, Optical Biopsy XVIII: Toward Real-Time Spectroscopic Imaging and Diagnosis; 112340O (2020) https://doi.org/10.1117/12.2545618
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Antibiotic resistance is a burgeoning global public health threats of our time. Antibiotic resistance is a multifactorial and complex problem which cannot be solved by only developing stronger and better antibiotic compounds. Rapid detection and characterization of pathogenic bacteria are critical for effectively treating bacterial infections without exacerbating the resistance problem. Here, we present a novel highly-sensitive and label-free platform, Rapid-Ultra-Sensitive-Detector (RUSD), that utilizes the high reflectance coefficient of light at the interface between low-refractive-index and high-refractive-index media. The sensitivity of RUSD is three to four orders of magnitude higher than conventional optical density-based methods. Utilizing RUSD, we can detect as low as ~20 bacterial cells or a single fungal cell. This technique does not require any sophisticated signal processing steps and it enables growth rate measurements in less than an hour. Finally, we can now measure antibiotics resistance of several gram-negative and gram-positive bacteria, including Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, within two hours.
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Chi Shu, Hanshu Yan, Kan Lin, Chwee Ming Lim, Wei Zheng, Jiashi Feng, and Zhiwei Huang "Enhancing in vivo nose cancer detection with rapid fiberoptic Raman and deep learning techniques (Conference Presentation)", Proc. SPIE 11234, Optical Biopsy XVIII: Toward Real-Time Spectroscopic Imaging and Diagnosis, 112340O (9 March 2020); https://doi.org/10.1117/12.2545618
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KEYWORDS
Raman spectroscopy
In vivo imaging
Fiber optics
Cancer
Diagnostics
Nose
Neural networks
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