A simple AI-enabled method for quantifying bacterial adhesion on dental materials

Purpose: Measurement of bacterial adhesion has been of great interest for different dental materials. Various methods have been used for bacterial counting; however, they are all indirect measurements with estimated results and therefore cannot truly reflect the adhesion status. This study provides a new direct measurement approach by using a simple artificial intelligence (AI) method to quantify the initial bacterial adhesion on different dental materials using Scanning Electron Microscope (SEM) images. Materials and Methods: Porphyromonas gingivalis (P.g.) and Fusobacterium nucleatum (F.n.) were used for bacterial adhesion on dental zirconia surfaces, and the adhesion was evaluated using SEM images at time points of one, seven, and 24 h(s). Image pre-processing and bacterial area measurement were performed using Fiji software with a machine learning plugin. The same AI method was also applied on SEM with Streptococcus mutans (S.m.) inoculated PMMA nano-structured surface at 1, 24, 72, and 168 h(s), and then further compared with the CLSM method. Results: For both P.g. and F.n. initiation adhesion on zirconia, a new linear correlation (r² > 0.98) was found between bacteria adhered area and time, such that: $$ For S.m. on PMMA surface, live/dead staining CLSM method and the newly proposed AI method on SEM images were strongly and positively associated (Pearson's correlation coefficient r > 0.9), i.e. both methods are comparable. Conclusions: SEM images can be analyzed directly for both morphology and quantifying bacterial adhesion on different dental materials' surfaces by the simple AI-enabled method with reduced time, cost, and labours.