## 4 Discussion
It is important to note that, unlike in their research, we did not apply any additional pre-processing to the images, which may have contributed to the differences in the results. In the study conducted by [PERSON] et al. [27], they used cropped images of teeth from panoramic radiographs, specifically teeth numbers 16, 26, 36, and 46, and employed the ResNet-152 network for dental age estimation. They reported an accuracy range between 89.05% and 90.27% in their findings. Similar studies can be found in the literature wherecropped panoramic images were used [28]. Using a deeper network and providing a smaller region of interest instead of a broad panoramic view for AI training may have contributed to higher performance. [PERSON] et al. [13] used 15,000 panoramic radiographs and Darknet-19, and they reported an accuracy of approximately 84% and 96% when the acceptable range of dental estimation was \(\pm\)5 and \(\pm\)10 years, respectively. They also reported a mean error between actual age and predicted age of 2.74 and 3.49, respectively. In our study, using DarkNet-19, which showed the highest accuracy in age estimation, we achieved an accuracy of 80.59% and an MAE of 0.42 \(\pm\) 1.18641. We believe that the more significant number of panoramic radiographs used in [PERSON] et al. 's study may have contributed to a higher accuracy than in ours. Although there is no definitive consensus in the literature, it is generally reported that at least one thousand examples per class are necessary for AI image classification. The most significant limitation of this study is that it was conducted with a dataset that cannot be considered large due to the inherent constraints of the healthcare field. Another limitation worth mentioning is that the images needed to be pre-processed. Similar studies that included pre-processing have observed higher success rates. Future studies could utilize larger datasets with pre-processed images. Additionally, instead of the conventional CNNs we tested, networks specifically designed for medical imaging could be developed. The following supporting information can be downloaded at this location: [[https://www.mdpi.com/article/10.3390/app14167014/s1](https://www.mdpi.com/article/10.3390/app14167014/s1)]([https://www.mdpi.com/article/10.3390/app14167014/s1](https://www.mdpi.com/article/10.3390/app14167014/s1)), Table S1. Post hoc Tukey test results for the CNNs' Mean Absolute Error (MAE) data. Conceptualization, T.N.S. ; Methodology, T.K. ; Software, T.K. ; Formal analysis, T.K. ; Investigation, T.N.S. ; Data curation, T.N.S. ; Writing--original draft, T.N.S. ; Writing--review & editing, T.K. ; Visualization, T.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The ethics approval was obtained from the Karamanoglu Mehmetbey University Faculty of Medicine Clinical Research Ethics Committee on 26 July 2022 (Approval code: 07-2022/19). The study was conducted following the Declaration of Helsinki and approved by the Karaman Provincial Health Directorate. Written informed consent has been obtained from the patients/legal guardians. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to Personal Data Protection Law in our country. The authors declare no conflicts of interest.