Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review

TL;DR

This systematic literature review evaluates CNN-based approaches for dental radiography segmentation across modalities (2D and 3D). Using a rigorous SLR, it synthesizes 45 studies (2014–2023), detailing architectures, frameworks, evaluation metrics, and datasets while highlighting the predominance of private data and limited public resources. The review finds CNNs (e.g., U-Net, Mask R-CNN) to deliver strong segmentation and detection performance (IoU/DSC-based metrics) but notes generalizability challenges due to data diversity and imaging sources. It calls for larger, diverse, externally validated datasets and more interpretable models to enable robust clinical translation and reproducible benchmarking.

Abstract

In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.

Figures (11)

• Figure 1: The Subsets of Artificial Intelligence.
• Figure 2: Common Deep Learning Applications.
• Figure 3: Outline of CNN.
• Figure 4: Taxonomy of Deep Learning models.
• Figure 5: Comparison of Reinforcement Deep Learning Cycle and Reinforcement Learning Cycle