Machine Learning Applications in Detecting Caries and Periodontal Disease from Intraoral Images

Abstract

The application of machine learning (ML) in dentistry has expanded significantly, promising enhanced diagnostic accuracy and efficiency. This study reviews ML techniques employed in detecting dental caries and periodontal diseases using intraoral images [1]. Traditional diagnostic methods often involve subjective assessment, potentially leading to diagnostic inconsistencies. Advanced ML algorithms, particularly convolutional neural networks (CNNs), offer objective, rapid, and accurate analysis of dental imagery. This review summarizes current methodologies, examines dataset characteristics, and evaluates algorithm performances reported in recent literature. Findings indicate that CNN-based systems achieve high accuracy, sensitivity, and specificity, significantly outperforming traditional radiographic interpretation. Challenges including dataset limitations, image quality variability, and model generalization are addressed. The review highlights the importance of integrating diverse and high-quality image datasets to enhance the robustness and generalizability of ML models. Additionally, the potential of ML techniques to streamline clinical workflows, reduce diagnostic time, and improve early intervention strategies is discussed. Ethical considerations, such as transparency in algorithm decision-making proces ses and ensuring patient data privacy, are also emphasized. Conclusively, ML applications in intraoral diagnostics represent a transformative advancement in dental care, underscoring the necessity for standardized image acquisition protocols, comprehensive clinical validation studies, and extensive, diverse datasets to ensure practical implementation and widespread clinical adoption.