## 5 Results and Findings
### Deep Learning Approaches  
A thorough summary of recent work in the field of deep learning as it relates to dental image processing is given in Table 4. It covers a wide range of applications and demonstrates how different deep learning models work efficiently to handle specific problems in dental healthcare:  
A. Dental applications ranging from digital diagnostics, caries detection, age and gender categorization, tooth segmentation, disease identification, and more are covered in these articles. This diversity highlights how deep learning may be tailored to address different elements of dental treatment. Researchers use many deep learning models, each one created especially for a particular objective. Known architectures such as CNN, VGG16, ResNet, U-Net, and special structures are among these models. The complexity and requirements of the dental image processing task often influence the model selection. A variety of pictures are used in dental image analysis, such as including periodontal radiographs, dental X-rays, panoramic radiographs, 3D point cloud data, and near-infrared transillumination images. Deep learning algorithms have been shown to be efficient in analyzing and deciphering these various types of images. Depending on the particular application, performance measurements might vary, but they often include IoU scores, accuracy, sensitivity, specificity, and precision. These measurements show how deep learning models can reliably and accurately complete tasks involving the interpretation of dental images. While dataset sizes are not explicitly mentioned in the table, it is essential to remember that they can significantly impact how well a model performs when used for training and testing. More accurate and reliable models are typically linked to larger datasets. The findings of these studies have significant clinical impacts. Because AI-driven solutions are more precise and accurate, they may enhance patient care, diagnosis, and treatment planning. As a result, both patient outcomes and the efficiency of dental offices would increase. Dental image analysis using deep learning has a lot of potential, however, there are still issues with data collection, annotation, privacy, and clinical validation. Standardized datasets are emphasized as being essential for establishing fair model comparisons, benchmarking diagnostic systems, and encouraging the creation of AI solutions that can be used in a variety of clinical contexts. Table 4 summarizes the latest developments in deep learning for dental image analysis, demonstrating the technology's adaptability and potential to transform dental care completely. It highlights the variety of applications, model selections, and types of images while acknowledging the performance metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Ref, Year** & **Application** & **Model** & **Images Type** & **Performance Metric** \\ \hline \multirow{2}{*}{1} & [19], 2022 & Digital dental diagnosis using & \multirow{2}{*}{CNN, VGG16.} & \multirow{2}{*}{\begin{tabular}{c} Periodontal \\ radiograph. \\ \end{tabular} } & Accuracy: \\  & & deep learning. & & CNN: 77.78\%; \\  & & & VGG16: 98.48\%. \\ \hline \multirow{2}{*}{2} & [20], 2023 & Deep learning (DL)-based & \multirow{2}{*}{\begin{tabular}{c} U-Net like \\ structure. \\ \end{tabular} } & \multirow{2}{*}{Dental X-ray images.} & \multirow{2}{*}{—} \\  & deformable registration & & & \\  & framework. & & & \\ \hline \multirow{2}{*}{3} & [21], 2022 & Classifying types of canine & \multirow{2}{*}{\
\begin{tabular}{c} DenseNet-121, \\ VGG-16, Inception \\ V3, ResNet-50. \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Panoramic dental \\ radiographs. \\ \end{tabular} } & Accuracy \\  & & DenseNet = 68.52\%; \\  & & VGG = 57.41\%; \\  & & & ResNet = 87.04\%; \\  & & & Inception = 92.59\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the selected studies that use deep learning approaches.