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13 March 2024 Deep-learning aided multispectral autofluorescence imaging for rapid bioburden detection and gram-type classification on diabetic foot ulcers
Aravind Sridhar, Ragavendiran Balasubramanian, Jagdish A. Krishnaswamy, Geethanjali Radhakrishnan, Bala Pesala
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Proceedings Volume PC12832, Optics and Biophotonics in Low-Resource Settings X; PC1283208 (2024) https://doi.org/10.1117/12.3003591
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Rapid, point-of-care bacterial bioburden detection on diabetic foot ulcers, burn wounds, and surgical site wounds, and subsequent gram-type classification is a challenge that cannot be addressed in low-resource settings. This is because current gold-standard practices based on cell culture take 2-3 days for a pathogen detection and classification. Further, these methods are cumbersome, require sophisticated microbiological facilities, and trained personnel. To address this issue, we demonstrate a rapid (<2 min), point-of-care platform, Illuminate®, that combines autofluorescence intensity imaging and deep learning to detect bacterial bioburden. We use a UNET deep learning model with a DenseNet 201 backbone to detect bioburden spatially and, to further classify the gram-type. Our initial results show a >80% accuracy in detecting bioburden and a >70% accuracy in gram-type classification. We further use this platform for identifying clinically relevant bacteria such as pseudomonas aeruginosa that cause antimicrobial resistance.
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Aravind Sridhar, Ragavendiran Balasubramanian, Jagdish A. Krishnaswamy, Geethanjali Radhakrishnan, and Bala Pesala "Deep-learning aided multispectral autofluorescence imaging for rapid bioburden detection and gram-type classification on diabetic foot ulcers", Proc. SPIE PC12832, Optics and Biophotonics in Low-Resource Settings X, PC1283208 (13 March 2024); https://doi.org/10.1117/12.3003591
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KEYWORDS
Auto-fluorescence imaging
Bacteria
Diabetic foot ulcers
Autofluorescence
Multispectral imaging
Deep learning
Education and training
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