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In Vitro Assay of Bacterial Adhesion onto Mammalian Epithelial Cells
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A simple AI-enabled method for quantifying bacterial adhesion on dental materials.

Hao Ding1, Yunzhen Yang1, Xin Li2,3

  • 1Dental Materials Science, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong.

Biomaterial Investigations in Dentistry
|September 9, 2022
PubMed
Summary

A new artificial intelligence (AI) method directly quantifies bacterial adhesion on dental materials using Scanning Electron Microscope (SEM) images. This AI approach offers a faster, cheaper, and more accurate alternative to traditional methods for dental material research.

Keywords:
BacteriaPMMAartificial intelligencedental materialszirconia

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Area of Science:

  • Dental Materials Science
  • Microbiology
  • Artificial Intelligence in Imaging

Background:

  • Bacterial adhesion to dental materials is crucial for material performance and oral health.
  • Current methods for quantifying bacterial adhesion are indirect and provide estimated results.
  • A direct, accurate, and efficient method for measuring bacterial adhesion is needed.

Purpose of the Study:

  • To develop and validate a simple artificial intelligence (AI) method for direct quantification of initial bacterial adhesion on dental materials.
  • To utilize Scanning Electron Microscope (SEM) images for AI-driven bacterial adhesion analysis.
  • To compare the AI method with existing techniques for bacterial quantification.

Main Methods:

  • Dental zirconia and PMMA nano-structured surfaces were inoculated with specific oral bacteria (Porphyromonas gingivalis, Fusobacterium nucleatum, Streptococcus mutans).
  • Bacterial adhesion was evaluated using SEM images at various time points.
  • Image pre-processing and bacterial area measurement were performed using Fiji software with a machine learning plugin.
  • The AI method was validated against the Confocal Laser Scanning Microscopy (CLSM) live/dead staining method.

Main Results:

  • A strong linear correlation (r² > 0.98) was observed between the adhered bacterial area and time for P. gingivalis and F. nucleatum on zirconia.
  • The AI method showed strong positive association (Pearson's correlation coefficient r > 0.9) with the CLSM method for S. mutans on PMMA.
  • The AI method demonstrated comparability with established bacterial quantification techniques.

Conclusions:

  • SEM images can be directly analyzed for bacterial adhesion quantification using a simple AI-enabled method.
  • The proposed AI method offers a direct, efficient, and potentially more accurate approach to studying bacterial adhesion on dental materials.
  • This AI method reduces time, cost, and labor compared to traditional bacterial counting techniques.