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13 March 2024 An accurate and rapid method (<10 min) to detect biofilms in wounds using a combination of wound blotting, white light imaging, and machine learning
Jagdish A. Krishnaswamy, Keerthana S., Krishna Sree S., Janakrai N. Parekh, Geethanjali Radhakrishnan, Bala Pesala
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Proceedings Volume PC12832, Optics and Biophotonics in Low-Resource Settings X; PC128320E (2024) https://doi.org/10.1117/12.3003622
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Bacterial biofilms on wounds lead to severe infections because of their resistance to antibiotics and host defence mechanisms. These bacterial colonies comprising of bacterial cells embedded in an extracellular matrix can easily develop on wounds, are hard to detect, and significantly delay wound healing leading to chronic infections especially in diabetic foot ulcer patients. Current methods to detect bacterial biofilms are extremely cumbersome and time-consuming (2-3 days) and, therefore, pose a challenge to low-resource implementations. We demonstrate a machine learning aided rapid wound blot detection method (<10 min) that combines the wound blotting technique using nitrocellulose membranes, white light imaging and machine learning-based models to accurately infer the presence of biofilms. We validate our method against the standard test-tube method that utilizes dye staining of the wound-swab culture to infer biofilm presence and demonstrate a detection accuracy in excess of 85%.
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Jagdish A. Krishnaswamy, Keerthana S., Krishna Sree S., Janakrai N. Parekh, Geethanjali Radhakrishnan, and Bala Pesala "An accurate and rapid method (<10 min) to detect biofilms in wounds using a combination of wound blotting, white light imaging, and machine learning", Proc. SPIE PC12832, Optics and Biophotonics in Low-Resource Settings X, PC128320E (13 March 2024); https://doi.org/10.1117/12.3003622
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KEYWORDS
Biofilms
Machine learning
Bacteria
Wound healing
Gold
Mathematical optimization
Matrices
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