## 5 Results and Findings
### Machine Learning Approaches  
A thorough overview of current dental image analysis research is provided in Table 5, which also emphasizes the use of machine learning algorithms for a range of dental diagnostic and analytical applications. A wide range of dental image analysis applications are covered by the chosen studies. Dental disease classification, tooth segmentation, caries detection, age and gender classification, digital dental diagnosis, and more are all included in these applications. This variety demonstrates how machine learning can be used to address a variety of dental healthcare issues. Academics utilize various machine learning models customized for their individual research goals. These models include well-known architectures such as decision tree, naive Bayes, multilayer perceptron, KNN, ANNs, and custom structures. The research uses various dental imaging modalities including periodontal radiographs, dental X-ray images, panoramic dental radiographs, 3D point cloud data, near-infrared transillumination images, and more. This illustrates the variety of dental imaging modalities and the ways machine learning can be adapted to fit these modalities. The evaluation of model performance relies on a range of metrics, including accuracy, sensitivity, specificity, precision, intersection over union (IoU), and area under the curve (AUC). These metrics ensure a comprehensive assessment of the effectiveness of machine learning models in dental image analysis. There are essential clinical implications for the results shown in Table 5. The reliability of AI-assisted diagnoses and treatment planning can be improved by utilizing machine learning models that have been trained on vast and varied datasets and can enhance the reliability of AI-assisted diagnoses and treatment planning. The value of standardized datasets for comparing diagnostic systems is highlighted by a number of studies. Standardization makes it easier to compare models fairly and promotes the creation of reliable AI solutions that work in a variety of clinical contexts. Table 5 encompasses research conducted from 2018 to 2023, demonstrating the ongoing evolution of machine learning techniques in dental imaging. Researchers continue to explore innovative approaches to overcome challenges and enhance the generalization of machine learning models in dentistry. Table 5 provides an overview into the promising landscape of machine learning in dental image analysis. These advancements hold the potential to revolutionize dental diagnostics and treatment planning, ultimately benefiting both clinicians and patients. Nonetheless, challenges related to dataset size and clinical validation of AI models remain areas of ongoing research and development within this field. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Ref, Year** & **Application** & **Model** & **Images Type** & **Performance Metric** \\ \hline
30 & [48], 2023 & \begin{tabular}{c} Dental disease classification and \\ segmentation. \\ \end{tabular} & \
\begin{tabular}{c} Inception \\ ResNetv2. \\ \end{tabular} &
\begin{tabular}{c} Dental X-ray images. \\ \end{tabular} & Accuracy 94.51\%. \\ \hline
31 & [49], 2023 & \
\begin{tabular}{c} Endodontic treatment outcome \\ prediction. \\ \end{tabular} & CNN. &
\begin{tabular}{c} Preoperative dental \\ radiographs. \\ \end{tabular} & N/A. \\ \hline
32 & [50], 2023 & \
\begin{tabular}{c} Tooth numbering and \\ diagnosis. \\ \end{tabular} & VGG-16. &
\begin{tabular}{c} Optical radiographic \\ images. \\ \end{tabular} & Accuracy 89.8\%. \\ \hline
33 & [51], 2023 &
\begin{tabular}{c} Dental disease classification \\ (e.g., oral cancer, caries). \\ \end{tabular} & CNN. & Dental images. & Accuracy 95.34\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._