Light scattering-based screening method for rapid evaluating antibiotic effects on bacteria using laser speckle imaging
Abstract Rapid and timely selection of appropriate antibiotics minimizes treatment delays, enhances patient care, and improves infection control. The antibiotic disk diffusion method (Kirby–Bauer test) is the most widely used growth-based technique for assessing bacterial susceptibility due to its s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | Journal of Biological Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13036-025-00542-8 |
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| Summary: | Abstract Rapid and timely selection of appropriate antibiotics minimizes treatment delays, enhances patient care, and improves infection control. The antibiotic disk diffusion method (Kirby–Bauer test) is the most widely used growth-based technique for assessing bacterial susceptibility due to its simplicity and reliability. However, conventional growth-based methods typically require over 12 h of incubation for visible inspection, making them unsuitable for situations requiring urgent treatment. In this study, we developed a novel laser speckle imaging (LSI) system that measures light scattering properties in a medium, along with an advanced image processing method for the quantitative assessment of antimicrobial effects based on bacterial activity. The LSI system utilizes an optical diffuser with controlled rotations to generate multiple independent speckle illumination patterns. The image processing algorithm analyzes correlation contrast in time-series laser speckle images, enabling more precise bacterial activity detection compared to conventional LSI techniques. The proposed method successfully detected effective antibiotics within 3h for both Gram-negative and Gram-positive bacteria, a capability not achievable using traditional bacterial growth-based antimicrobial susceptibility tests. This approach has the potential to serve as a versatile, rapid, and clinically viable tool for identifying effective antibiotics in patients with bacterial infections, significantly improving diagnostic efficiency in clinical settings. |
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| ISSN: | 1754-1611 |