Using machine learning for improving knowledge on antibacterial effect of bioactive glass

The aim of this work was to find relationships between critical bioactive glass characteristics and their antibacterial behaviour using an artificial intelligence tool. A large dataset including ingredients and process variables of the bioactive glasses production, bacterial characteristics and microbiological experimental conditions was generated from literature and analyzed by neurofuzzy logic technology. Our findings allow an explanation on the variability in antibacterial behaviour found by different authors and to obtain general conclusions about critical parameters of bioactive glasses to be considered in order to achieve activity against some of the most common skin and implant surgery pathogens.