A simple AI-enabled method for quantifying bacterial adhesion on dental materials. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Ding, Hao; Yang, Yunzhen; Li, Xin; Cheung, Gary Shun-Pan; Matinlinna, Jukka Pekka; Burrow, Michael; Tsoi, James Kit-Hon
• Is Part Of: Biomaterial investigations in dentistry. Volume 9:Issue 1(2022)
• Page Start: 75
• Page End: 83
• Publication Date: 2022-12-31
• Subjects: Bacteria -- artificial intelligence -- zirconia -- PMMA -- dental materials; Dental materials -- Periodicals; Dentistry -- Periodicals; Biomedical and Dental Materials; Periodical; 617.695
• DOI: 10.1080/26415275.2022.2114479
• Languages: English
• ISSNs: 2641-5275

Abstract

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.