Karthika Mohan
Independent Researcher
Kerala, India
Abstract
Dental caries remains one of the most prevalent chronic diseases among children globally, with early detection being critical to prevent progressive enamel and dentin damage. Pediatric dentistry faces a unique challenge in accurately diagnosing carious lesions due to variations in child behavior, enamel maturation, and lesion progression. Artificial intelligence (AI) has emerged as a transformative solution, with machine learning and image recognition technologies showing promise in automating diagnostic processes. This study presents a comprehensive clinical evaluation of AI-assisted caries detection models used in pediatric dentistry, exploring their diagnostic accuracy, reliability, and feasibility in a real-world clinical setting. The research compares AI-assisted diagnostics with traditional visual-tactile methods and radiographic interpretations in children aged 6–12 years. By evaluating sensitivity, specificity, and predictive value across multiple diagnostic frameworks, this paper establishes the clinical potential of AI in early caries detection and decision support in pediatric oral health care.
Keywords
Pediatric Dentistry, Dental Caries, Artificial Intelligence, Machine Learning, Caries Detection, Diagnostic Accuracy, Clinical Evaluation, Image Recognition
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