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

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

SUBMITTER: Ding H 

PROVIDER: S-EPMC9448434 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature