AI-Enabled Dental Imaging for Oral Disease Detection

Abstract

This study aims to enhance the accuracy and efficiency of dental diagnostics using deep learning models to automatically detect and classify oral disease and dental structures on panoramic radiographs. By employing a quantitative research methodology, the study evaluates key performance metrics. The model's ability to accurately detect dental anomalies, such as caries, periodontal conditions, restorations, and implants, shows significant promise for real-time clinical applications (Arsiwala-Scheppach et al., 2023). In parallel, the study explores the broader potential of AI in dental imaging, addressing current limitations such as the adaptability of models trained on narrow datasets. It emphasizes the importance of expanding datasets to capture a wider range of patient demographics and imaging modalities and highlights the need to validate AI models in real-world clinical settings (Putra et al., 2022). Together, this research underscores the transformative role of AI in modernizing dental diagnostics, moving from traditional methods to advanced AI-enhanced techniques (Patil et al., 2022).