THE POTENTIAL OF USING AI IN THE EARLY DIAGNOSIS OF CARIES IN PEDIATRIC DENTISTRY

The article provides a scientific analysis of the use of artificial intelligence (AI) technologies in the early diagnosis of dental caries in pediatric dentistry. In recent years, highly accurate neural networks and automated image processing systems have significantly improved the ability to detect caries at its initial stages. Compared to traditional clinical examinations and radiography, AI-based diagnostics offer advantages such as speed, precision, and reduced subjective errors. Research findings show that AI-supported diagnosis enhances the effectiveness of preventive dentistry in children, reduces the need for invasive treatments, and plays a crucial role in improving oral health.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-30
DOI
https://doi.org/10.5281/zenodo.17236903
Primary Topic
Data Analysis with R
Type
article
Field-Weighted Citation Impact
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article

THE POTENTIAL OF USING AI IN THE EARLY DIAGNOSIS OF CARIES IN PEDIATRIC DENTISTRY

Zenodo (CERN European Organization for Nuclear Research)
Data Analysis with R
article

THE POTENTIAL OF USING AI IN THE EARLY DIAGNOSIS OF CARIES IN PEDIATRIC DENTISTRY

article en

Abstract

The article provides a scientific analysis of the use of artificial intelligence (AI) technologies in the early diagnosis of dental caries in pediatric dentistry. In recent years, highly accurate neural networks and automated image processing systems have significantly improved the ability to detect caries at its initial stages. Compared to traditional clinical examinations and radiography, AI-based diagnostics offer advantages such as speed, precision, and reduced subjective errors. Research findings show that AI-supported diagnosis enhances the effectiveness of preventive dentistry in children, reduces the need for invasive treatments, and plays a crucial role in improving oral health.

Zenodo (CERN European Organization for Nuclear Research)
University of Dental Medicine (MM), University of Embu (KE)
Openalex Percentile: Top 25%
Data Analysis with R
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