Deep Learning Methods for Tooth Detection and Classification in Various Dental Image Datasets: A Taxonomy and Future Directions

Abstract

Deep learning approaches have made significant advancements in recent years, generating considerable interest in using them for medical image analysis. In dentistry, the precision of tooth detection and classification serves as the cornerstone of dental practice as it can identify the presence of dental abnormalizes at an early stage. This paper presents an exploration of the potential of deep learning methods for tooth detection and classification across a variety of dental imaging datasets including radiographs, cone-beam computed tomography (CBCT) scans, and photograph images. Convolutional Neural Networks (CNNs) have emerged as one of the most widely used and effective deep learning methods in the field of dental disease diagnosis and medical image analysis. The study aims to conceptualize how these models can effectively learn intricate tooth features, despite having variations in tooth morphology, image quality, and imaging techniques. It highlights the increasing role of deep learning in diagnosing dental diseases and emphasizes the importance of accurate tooth classification for effective treatment planning. The study reviews existing research in deep learning-based tooth classification, discusses challenges including dataset scarcity and model interpretability, and suggests future directions.