## 5 Results and Findings
### Thematic Clusters  
As can be seen in Figure 6, in analyzing the data extracted from the VOSviewer version 1.6.20 regarding primary methods and algorithms in AI-based dental image analysis, several key insights emerge that shape the understanding of this field and its future direction. This analysis focuses on clusters of terms that highlight the central methodologies and areas of focus within AI-driven dental imaging research. The depth of interconnection among various terms allows for a comprehensive understanding of the trends and gaps in the literature. The first and most prominent cluster focuses heavily on deep learning and convolutional neural networks (CNNs), indicating that these methods are central to AI-based dental image analysis. The strong links with terms such as image segmentation, dental images, and image classification reflect the dominant role of CNNs in t  
Figure 6: Thematic clusters of the top keywords, self-extract from VOSviewer. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**No. ** & **Ref., Year** & **Application** & **Model** & **Images Type** & **Performance Metric** \\ \hline
16 & [67], 2021 & Predicting and detecting radiation-related dental caries. & Artificial neural network. & Panoramic radiography. & Sensitivity of 98.8\% (RRC detection) and 99.2\% (RRC prediction). & Sensitivity of 98.8\% (RRC detection) and 99.2\% (RRC prediction). & Sensitivity of 98.8\% (RRC detection) and 99.2\% (RRC prediction). & Sensitivity of 98.2\% (RRC prediction). \\ \hline
17 & [68], 2023 & Detection of dental cyst, tumor, and abscess lesions. & SVM. & Panoramic radiographs. & GLCM accuracy: 98\%; wavelet accuracy: 91\%; GLRLM accuracy: 95\%. \\ \hline
18 & [69], 2023 & Forensics age estimation. & CGAN, DASNET. & Dental image. & Accuracy: 87\%; coefficient of determination (R2): 0.85; absolute error: 1.18 years. & Accuracy: 87\%; coefficient of determination (R2): 1.85; absolute error: 1.  
classifying, and segmenting dental images, particularly in applications like detecting dental caries, diagnosing diseases, and performing tooth radiography. Convolution and deep neural networks are also strongly associated, showing that CNN architectures are widely employed due to their ability to extract relevant features from complex image datasets with high accuracy. The inclusion of terms such as feature extraction, medical imaging, and segmentation demonstrates that deep learning algorithms are not only applied in basic image classification tasks but also in more complex and detailed procedures like image segmentation and object detection, where precise identification of dental structures is critical for diagnostics and treatment planning. The growing adoption of deep learning can be seen as a response to the need for automation and accuracy in analyzing large-scale, high-resolution dental images. In summary, deep learning, and specifically CNNs, are the core methodologies driving advances in AI-based dental image analysis. Another prominent cluster revolves around artificial intelligence and machine learning. While deep learning techniques are a subset of machine learning, this cluster highlights the broader application of AI algorithms beyond neural networks. Terms such as dental caries, diagnostic imaging, controlled study, and diagnostic accuracy suggest that AI is being applied to improve diagnostic precision and enhance clinical outcomes. The relatively high emphasis on machine learning and controlled studies points to the focus on ensuring the clinical validity of AI models through rigorous testing and validation. Additionally, terms such as predictive value, sensitivity, and specificity highlight the focus on performance evaluation metrics in the dental field, ensuring that AI-based tools not only provide accurate diagnoses but are also reliable in predicting outcomes, minimizing false positives and negatives in dental diagnostics. The third cluster emphasizes image processing and related diagnostic applications.