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Teeth

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...

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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

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AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several

Oana-Maria Butnaru¹, Monica Tatarciuc², Ionut Luchian³

¹Department of Biophysics, Faculty of Dental Medicine, "Grigore T. Popa" University of Medicine and Phamacy, 700115 Iasi, Romania.

Medicina (Kaunas, Lithuania)

|April 26, 2025

Summary

Artificial intelligence (AI) shows strong potential in dental diagnostics, matching senior specialists in accuracy for detecting attachment and bone loss. This AI tool can enhance diagnostic precision and patient outcomes in dentistry.

Keywords:

AI alveolar bone resorption bone loss dental imaging periodontal pocket periodontal status periodontitis

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Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging

Background:

Artificial intelligence (AI) is increasingly utilized in healthcare for its data analysis capabilities.
AI offers potential for enhancing speed and precision in dental and periodontal diagnostics.

Purpose of the Study:

To evaluate the diagnostic reliability of AI in dental and periodontal assessments.
To compare AI-assisted diagnoses against those made by general dentists, specialists, and senior specialists.

Main Methods:

A comparative study involved 60 dental practitioners (general dentists, specialists, senior specialists) and an AI system.
Participants analyzed six panoramic radiographic images; diagnoses were compared to a gold standard.
Statistical analysis included chi-square tests and ANOVA to compare diagnostic performance.

Main Results:

AI demonstrated consistency comparable to senior specialists in identifying subtle dental and periodontal conditions.
Both AI and senior specialists excelled in detecting attachment loss and alveolar bone loss.
AI achieved a mean score of 6.12 for attachment loss detection, outperforming other groups.

Conclusions:

AI systems show significant potential as reliable tools for dental and periodontal assessment.
AI can complement human expertise, enhancing diagnostic precision and patient outcomes.
Further clinical validation is needed to address AI limitations like algorithmic bias.