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Sci Rep. 2016 Sep 7;6:32694. doi: 10.1038/srep32694.

A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis.

Author information

1
Physical Sciences of Imaging for Biomedical Sciences (PSIBS) Doctoral Training Centre, College of Engineering &Physical Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
2
School of Dentistry, Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Mill Pool Way, Birmingham, B5 7EG, UK.
3
School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Abstract

Biofilm accumulation on biomaterial surfaces is a major health concern and significant research efforts are directed towards producing biofilm resistant surfaces and developing biofilm removal techniques. To accurately evaluate biofilm growth and disruption on surfaces, accurate methods which give quantitative information on biofilm area are needed, as current methods are indirect and inaccurate. We demonstrate the use of machine learning algorithms to segment biofilm from scanning electron microscopy images. A case study showing disruption of biofilm from rough dental implant surfaces using cavitation bubbles from an ultrasonic scaler is used to validate the imaging and analysis protocol developed. Streptococcus mutans biofilm was disrupted from sandblasted, acid etched (SLA) Ti discs and polished Ti discs. Significant biofilm removal occurred due to cavitation from ultrasonic scaling (p < 0.001). The mean sensitivity and specificity values for segmentation of the SLA surface images were 0.80 ± 0.18 and 0.62 ± 0.20 respectively and 0.74 ± 0.13 and 0.86 ± 0.09 respectively for polished surfaces. Cavitation has potential to be used as a novel way to clean dental implants. This imaging and analysis method will be of value to other researchers and manufacturers wishing to study biofilm growth and removal.

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