# Root Dilaceration Using Deep Learning:
## 4 Discussion
\begin{table}
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**Author** & **Task** & **Type of Image** & **Model** & **Data Size** & **Metrics** \\ \hline [PERSON] et al. & Detection—dental anomaly & Panoramic & Faster RCNN & 23,000 & Precision: between 42–74\% \\  & & & & Sensitivity: between 27–100\% \\  & & & & Specificity: between 89–99\% \\ \hline \multirow{3}{*}{Welk} & Classification—dental anomaly & & ResNet-18 & & \\  & & & VGG11 & & Sensitivity (recall): 0.47 \\  & & & ResNet-50 & & Specificity: 0.59 \\  & & & VGG16 & & F1 score: 0.28 \\  & & & Inception v2 & & Accuracy: 0.57 \\  & & & Inception v3 & & \\ \hline \multirow{3}{*}{This work} & \multirow{3}{*}{Detection} & \multirow{3}{*}{Panoramic} & \multirow{3}{*}{19 Deep Learning Models} & \multirow{3}{*}{636} & mAP \\  & & & & Accuracy & Precision \\ \cline{1-1}  & & & Learning & Recall & \\ \cline{1-1}  & & & Models & & F1 Score \\ \cline{1-1}  & & & & Time Duration \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons with previous studies. Task indicates what kind of application was performed among classification, detection and segmentation. Metrics shows what kind of evaluation metrics were reported in the corresponding studies. * Treatment Planning: The detection of root dilacerations through deep learning can significantly influence treatment planning decisions. Clinicians can better anticipate the complexity and challenges associated with these conditions, leading to more informed treatment plans, including orthodontic interventions, surgical approaches, or alternative treatment options. * Enhanced Patient Care: By leveraging deep learning technology, clinicians can deliver more personalized and tailored care to patients with root dilacerations. This can lead to improved patient satisfaction, reduced treatment time, and enhanced treatment outcomes. Ultimately, it will provide increased efficiency and improved accuracy in dental healthcare. There are several limitations of this study that can positively affect the proposed results. The first limitation is the sample size. Considering the exclusion criteria, more data could increase the models' performances. Data augmentation techniques were not used because it was thought that the orientation of the image and the aspect ratio inherent in the PRs were unique for the detection of root dilaceration. On the other hand, this work was performed using panoramic images. They were routinely used during the first examination thanks to the wide range of scanning areas with very low radiation compared to 3D imaging modalities. If needed, 3D imaging modalities could also be used for further examinations to identify root dilaceration clinically. As a future perspective, there needs to be more collaboration between dental researchers, AI specialists, and ethicists to develop efficient, reliable, and ethical tools. Moreover, the creation of large, diverse, and high-quality datasets for training AI models should be prioritized, alongside robust data security and privacy measures. Lastly, further efforts should be made to integrate AI applications into dental practice management software to improve workflow efficiency. In the future, it is planned that models can be further improved with multi-centered large balanced datasets. Also, results will be compared with human observations on different levels.