Advancements in Dental Age Estimation: A Comprehensive Review of Convolutional Neural Networks and Ethical Considerations

Dental age estimation plays a crucial role in forensic odontology, orthodontics, and pediatric dentistry. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown promising results in automating dental age estimation processes. This paper presents a comprehensive survey of the application of CNNs in dental age estimation, covering various methodologies, datasets, and challenges. By reviewing evolution of dental age estimation techniques, explore the architecture and training strategies of CNNs, analyse benchmark datasets, and discuss the performance metrics commonly used. Furthermore, by examine the ethical considerations and limitations associated with CNN-based dental age estimation. Our survey provides insights into the current state-of-the-art, identifies research gaps, and offers suggestions for future directions in this rapidly evolving field.