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.

Peri-implantitis and peri-mucositis pose a severe threat to the success of dental implants. Current research focuses on the development of surfaces that inhibit biofilm formation while not inferring with tissue integration. This study compared the adherence of two oral bacterial species, Streptococcus sanguinis and Streptococcus mutans to nanostructured titanium surfaces. The samples included TiO2 nanotubes formed by anodization of titanium foil of 100, 50 and 15nm diameter (NT15, NT50, NT100), a nanoporous (15nm pore diameter) surface and compact TiO2 control. Adherent surviving bacteria were enumerated after 1h in an artificial saliva medium containing bovine mucin. Lowest numbers of adherent bacteria of both species were recovered from the original titanium foil and nanoporous surface and highest numbers from the Ti100 nanotubes. Numbers of attached S. sanguinis increased in the order (NT15