A simple AI-enabled method for quantifying bacterial adhesion on dental materials. Issue 1 (31st December 2022) - Ding, Hao; Yang, Yunzhen; Li, Xin; Cheung, Gary Shun-Pan; Matinlinna, Jukka Pekka; Burrow, Michael; Tsoi, James Kit-Hon
Abstract: 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 (r 2 > 0.98) was found between bacteria adhered area and time, such that: b acteria   adhered   area   ( m m 2 ) ∝ log ⁡ ( time ) 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.