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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.
Highlights
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
regions of interest (ROIs) were randomly placed on either the surface or the biofilm in the Scanning electron microscopy (SEM) images by the operator when training the algorithm. The reproducibility of this selection method was tested to determine whether the placement of the ROIs had an effect on the outcome of the segmentation
A large number of studies have reported the impact of experimental variables on biofilm disruption on native tissues and/or biomaterial surfaces but have used qualitative methods or methods that have high susceptibility to measurement bias
Summary
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. 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. Electron microscopy, light microscopy, bioluminescence imaging and macroscale photography have all been used as measurements to assess biofilm disruption[5,7,8,9,15,16,17,18,19,20,21,22,23,24,25] A gap exists to develop a methodology that can consistently, accurately and objectively segment biofilm from images to quantify biofilm disruption
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