Current State of Artificial Intelligence in Caries Detection: A Literature Review

Abstract

This review article investigates the application of artificial intelligence (AI) in dentistry, explicitly focusing on caries detection. After appropriate filtering, ten relevant studies were carefully examined from PubMed and IEEE Xplore. Bitewing radiographs emerged as the most frequently utilized imaging modality, followed by near infrared transillumination, periapical, intraoral, and radiovisiography images. Different neural networks were employed in these studies to detect the desired variables, and the quality and type of input data substantially impacted the outcomes. The article emphasizes the need for further research, particularly in exploring larger datasets and different image types to enhance the implementation of neural networks in caries detection. While the potential of AI in dentistry for caries detection is promising, continuous research and refinement of methods are vital to harness its capabilities thoroughly. Therefore, ongoing investigations are crucial to advancing dental practice through the effective utilization of AI. The article highlights the necessity for continued exploration and improvement in this evolving field.

Keywords: Artificial intelligence, Caries detection, Neural networks.

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