Detection Of Pulp Stones in Radiographic Images: Survey Using Artificial Intelligence

Pulp stones are calcifications within the dental pulp chamber that can complicate endodontic procedures and negatively impact dental health. Detecting pulp stones accurately is essential for effective diagnosis and treatment planning. Traditionally, manual inspection of radiographs by clinicians is employed for this task, but it is time-consuming and prone to variability. With advancements in artificial intelligence (AI), automated detection systems using deep learning techniques have emerged as promising alternatives. This literature survey reviews state-of-the-art AI-based methods for pulp stone detection, focusing on the application of machine learning and deep learning models. The review covers commonly used techniques, available datasets, evaluation metrics, and challenges faced in model development. A comprehensive analysis of the strengths and limitations of these approaches is provided, alongside a discussion on the potential for integrating AI into clinical practice. Future directions are suggested to address existing challenges, including the need for larger and more diverse datasets, improved model generalization, and better interpretability. This survey aims to guide researchers and practitioners in understanding the current landscape of AI applications in dental imaging, with a specific focus on the detection of pulp stones.