## 5 Results and Findings
### Deep Learning Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Ref, Year** & **Application** & **Model** & **Images Type** & **Performance Metric** \\ \hline
16 & [34], 2022 & \
\begin{tabular}{c} Automated measurement of \\ geometric parameters from \\ three-dimensional CBCT \\ images in natural head position. \\ \end{tabular} & Mask-RCNN. &
\begin{tabular}{c} Cone-beam \\ computed \\ tomography (CBCT) \\ images. \\ \end{tabular} & Reliability 98\%. \\ \hline
17 & [35], 2022 & \begin{tabular}{c} Developing a deep learning \\ model to classify C-shaped \\ canals in dental radiographs. \\ \end{tabular} & \
\begin{tabular}{c} Deep \\ convolutional \\ neural network. \\ \end{tabular} &
\begin{tabular}{c} Periapical and \\ panoramic dental \\ radiographs. \\ \end{tabular} & AUC: periapical 89\%; \\ \hline
18 & [36], 2021 & \begin{tabular}{c} The impact of image context on \\ tooth classification. \\ \end{tabular} & \
\begin{tabular}{c} Deep learning \\ models \\ (ResNet-34). \\ \end{tabular} &
\begin{tabular}{c} Panoramic dental \\ radiographs. \\ \end{tabular} & Accuracy of 93.3\% achieved \\ \hline
19 & [37], 2023 & \begin{tabular}{c} Age and gender assessment. \\ \end{tabular} & \begin{tabular}{c} Deep \\ convolutional \\ neural network \\ (DCNN). \\ \end{tabular} & \
\begin{tabular}{c} Dental radiographs. \\ \end{tabular} &
\begin{tabular}{c} Gender identification \\ accuracy of 91.7\%, age \\ identification accuracy of \\ 91\%. \\ \end{tabular} \\ \hline
20 & [38], 2022 & \begin{tabular}{c} Age and gender forecasting. \\ \end{tabular} & \
\begin{tabular}{c} Spike-neuron- \\ based \\ convolutional \\ neural network \\ (SN-CNN). \\ \end{tabular} &
\begin{tabular}{c} Dental images. \\ \end{tabular} & Accuracy of 99.6\%. \\ \hline
21 & [39], 2019 & \begin{tabular}{c} Caries detection. \\ \end{tabular} & \
\begin{tabular}{c} Convolutional \\ neural network \\ (CNN). \\ \end{tabular} &
\begin{tabular}{c} Near-infrared \\ transillumination \\ images. \\ \end{tabular} & IOU score of 72.7\%. \\ \hline
22 & [40], 2023 & \begin{tabular}{c} Gender classification. \\ \end{tabular} & Resnet50 network. & \
\begin{tabular}{c} Panoramic dental \\ X-ray images. \\ \end{tabular} &
\begin{tabular}{c} Classification accuracy of \\ 94\%. \\ \end{tabular} \\ \hline
23 & [41], 2023 & \begin{tabular}{c} Developing an intelligent \\ system for segmenting affected \\ parts in teeth and classifying \\ dental diseases. \\ \end{tabular} & \begin{tabular}{c} Intelligent \\ ant-lion-based \\ convolution neural \\ model (IALCNM). \\ \end{tabular} & \
\begin{tabular}{c} Dental images \\ showing affected \\ parts and dental \\ diseases. \\ \end{tabular} &
\begin{tabular}{c} Enhanced accuracy in \\ segmenting affected parts \\ and disease prediction. \\ \end{tabular} \\ \hline
24 & [42], 2021 & \
\begin{tabular}{c} Segmenting dental images \\ using threshold connected \\ component analysis. \\ \end{tabular} &
\begin{tabular}{c} Threshold- \\ connected \\ component (TCC). \\ \end{tabular} & Dental radiographs. & Accuracy 93\%. \\ \hline
25 & [43], 2020 & \
\begin{tabular}{c} Accuracy of dental visual assets \\ recognition and classification by \\ search engines. \\ \end{tabular} &
\begin{tabular}{c} SqueezeNet. \\ \end{tabular} & Dental visual assets. & Accuracy 93\%. \\ \hline
26 & [44], 2023 &
\begin{tabular}{c} Detecting dental caries using a \\ semisupervised approach. \\ \end{tabular} & Deeplabv3. & Dental radiography. & Accuracy 99.43\%. \\ \hline
27 & [45], 2023 & \begin{tabular}{c} Detection of endodontic \\ treatment obturation and \\ progression. \\ \end{tabular} & \
\begin{tabular}{c} YOLOv5s, \\ YOLOv5x, \\ YOLOv7 \\ deep-learning \\ models. \\ \end{tabular} &
\begin{tabular}{c} Dental radiographs. \\ \end{tabular} & Accuracy 95\%. \\ \hline
28 & [46], 2023 & \begin{tabular}{c} Teeth segmentation and \\ numbering. \\ \end{tabular} & \
\begin{tabular}{c} U-Net-based ROI \\ extraction, Hybrid \\ Task Cascade for \\ segmentation and \\ numbering \\ \end{tabular} & Panoramic \\ radiographs. &
\begin{tabular}{c} Precision 97\%; \\ recall 97\%; \\ IoU 92\%. \\ \end{tabular} \\ \hline
29 & [47], 2023 & \begin{tabular}{c} Caries detection. \\ \end{tabular} & \
\begin{tabular}{c} Curvilinear \\ Semantic Deep \\ CNN with \\ HBT-NF. \\ \end{tabular} &
\begin{tabular}{c} Dental X-ray images. \\ \end{tabular} & Accuracy 93.7\%. \\ \hline \hline \end{tabular}
\end{table}