Al-Based Detection of Dental Caries: Comparative Analysis with Clinical Examination

Abstract

ABSTRACT Dental caries pose a significant public health concern, affecting a vast population globally. Traditional clinical examination methods, although reliable, can be subject to human error and time-consuming. Artificial intelligence (AI) technologies have emerged as promising tools to enhance diagnostic accuracy and efficiency. This study explores the potential of AI in revolutionizing dental caries detection. Materials and Methods: A cohort of 50 patients with varying degrees of dental caries participated in this comparative analysis. Clinical examination by dental professionals served as the gold standard for caries detection. AI algorithms were trained using dental images, and their performance was evaluated against the clinical examination results. Results: The AI-based detection system demonstrated a sensitivity of 92% and a specificity of 85% in identifying dental caries, with an overall accuracy of 88%. The clinical examination yielded a sensitivity of 86% and a specificity of 90%, resulting in an overall accuracy of 88%. Statistical analysis indicated no significant difference between AI-based detection and clinical examination (P > 0.05). Conclusion: AI technology exhibits promise as an adjunctive tool for dental practitioners, potentially reducing diagnostic errors and improving efficiency. Integrating AI into routine dental practice may aid in early caries detection and promote better oral health outcomes.