Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Caries from Intraoral...

Area of Science:

• Dentistry
• Medical Imaging
• Artificial Intelligence

Background:

• Dental caries diagnosis relies heavily on radiographic interpretation.
• Early and accurate detection of dental caries is crucial for effective treatment.
• Current diagnostic methods may have limitations in accuracy and efficiency.

Purpose of the Study:

• To systematically review and assess the diagnostic accuracy of artificial intelligence (AI) models for dental caries detection and classification.
• To evaluate the performance of AI algorithms in analyzing dental radiography.
• To synthesize current evidence on AI's role in dental caries diagnostics.

Main Methods:

• A comprehensive systematic literature search was conducted across major databases (PubMed, Web of Science, SCOPUS, Embase).
• Inclusion criteria were applied to select relevant studies on AI models for dental caries.
• Ten articles were identified and analyzed for diagnostic accuracy of AI models.

Main Results:

• AI-based models exhibit high diagnostic accuracy in identifying dental caries.
• Analysis of dental radiography pictures by AI shows promising results.
• The reviewed studies indicate consistent performance of AI in caries detection.

Conclusions:

• AI models show significant potential for accurate dental caries detection and classification.
• AI analysis of dental radiographs offers a reliable diagnostic tool.
• Further research may validate AI's role in routine dental practice.