Instant scanner device for identifying wound infection utilizing Mie scatter spectra
AffiliationUniv Arizona, Biomed Engn Grad Interdisciplinary Program
KeywordsDiabetic foot ulcer
MetadataShow full item record
PublisherSPIE-INT SOC OPTICAL ENGINEERING
CitationRobin E. Sweeney, Elizabeth Budiman, Jeong-Yeol Yoon, "Instant scanner device for identifying wound infection utilizing Mie scatter spectra", Proc. SPIE 10215, Advanced Environmental, Chemical, and Biological Sensing Technologies XIV, 102150U (3 May 2017); doi: 10.1117/12.2267748; http://dx.doi.org/10.1117/12.2267748
Rights© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
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AbstractTissue biopsy and swab culture are the gold standards for diagnosing tissue infection; these tests require significant time, diagnostic costs, and resources. Towards earlier and specific diagnosis of infection, a non-destructive, rapid, and mobile detection device is described to distinguish bacterial species via light scatter spectra from the surface of an infected tissue, reagent-free. Porcine skin and human cadaveric skin models of wound infection were used with a 650 nm LED and an angular photodiode array to detect bacterial infections on the tissue surface, which can easily be translated to a typical CMOS array or smartphone. Tissue samples were inoculated with Escherichia coli, Salmonella Typhimurium, or Staphylococcus aureus and backscatter was collected from 100 degrees to 170 degrees in 10 degrees increments; each bacterial species resulted in unique Mie scatter spectra. Distinct Mie scatter spectra were obtained from epidermis (intact skin model) and dermis (wound model) samples, as well as from porcine and human cadaveric skin samples. Interactions between bacterial colonies and lipid particles within dermis samples generated a characteristic Mie scatter spectrum, while the lipid itself did not contribute to such characteristic spectrum as corroborated with body lotion experiments. The designed angular photodiode array is able to immediately and non-destructively detect tissue bacterial infection and identify the species of infection within three seconds, which could greatly improve point of care diagnostics and antibiotic treatments.
VersionFinal published version
SponsorsCardiovascular Biomedical Engineering Training Grant from U.S. National Institutes of Health (NIH) [T32HL007955]