EFFECTIVENESS OF AI MODELS IN EARLY DETECTION OF CARIES AND PERIODONTITIS: A STATISTICAL AND CLINICAL PERSPECTIVE

Abstract

Artificial Intelligence (AI) models have emerged as powerful tools for the early detection of dental
caries and periodontitis, enabling timely intervention to prevent disease progression. This study
evaluates the effectiveness of AI, particularly deep learning algorithms such as convolutional
neural networks (CNNs), in identifying preclinical stages of these conditions using dental
radiographs and clinical data. Statistical metrics, including sensitivity, specificity, precision, and
area under the receiver operating characteristic curve (AUC), are analyzed to assess model
performance. Challenges such as data quality, algorithmic bias, and clinical integration are also
discussed. The article highlights AI’s potential to enhance early diagnosis in dentistry and its future
implications.