## 2 Materials and Methods  
This study was conducted according to the Declaration of Helsinki and approved by the Karaman Provincial Health Directorate and the Karamanoglu Mehmetbey University Faculty of Medicine Clinical Research Ethics Committee with decision number 07-2022/19. Patient consent was obtained before the radiographs were taken and the consent forms have been archived at institutions. In machine learning processes, the prediction accuracy of trained models is closely related to the sample size, with accuracy tending to improve as the sample size increases. However, after reaching a certain point, often referred to as the \"sweet spot\", further increases in sample size do not significantly enhance the model's performance. Identifying this point is a state-of-the-art research topic in biostatistics, and unfortunately, there is currently no generally accepted rule. Since having a large sample size does not harm the artificial intelligence training process aside from extending the required training time, the approach of using \"the largest possible sample size\" was preferred in this research. The dataset consisted of 7667 panoramic radiographs obtained from patients aged 5-21, who did not have any systemic diseases; these radiographs were previously taken for diagnostic or treatment purposes at Karamanoglu Mehmetbey University's Ahmet Kelesoglu Faculty of Dentistry and Karaman Oral and Dental Health Center between 2016 and 2023. Out of a total of 8000 panoramic radiographs obtained using PCH-2500 (60-75 kVp, 5-10 mA, Vatech, Gyeonggi-do, Republic of Korea), Veraview IC5 HD (60-70 kV, 1-7.5 mA, Morita Corporation, Osaka, Japan), Hyperion X9 (60-75 kVp, 5-10 mA, My Ray, Imola, Italy), and Hyperion X5 (60-75 kVp, 5-10 mA, My Ray, Imola, Italy) panoramic radiography devices, 333 radiographs with imaging errors such as significant distortion, imaging errors due to patient positioning, or large ghost images were excluded from the study. To reflect reality more accurately, radiographs from patients with trans-palatal arches, braces, root canal treatments, caries, implants, bridges, and all types of restorations, hypodontia, or oligodontia were not excluded from the study. If present, metadata such as the name, date of birth, and radiograph capture date were cropped from specific radiographs. When building a DL system, a significant focus must be on avoiding overfitting. The learning capacity must be appropriate, considering the amount of data and the difficulty of the task. Training, validation, and testing must be used to correctly verify which accuracy a DL system will achieve for future unseen data. This means that the acquired dataset must be split into three parts. The training set (1) is used to learn which patterns in the images correlate with the labels. For the validation set (2), once these patterns are learned, a different set of images is used to verify whether the model is under- or overfitting. Based on this assessment, the learning capacity of the network can be adapted to better fit the data. Regarding test set (3), since design choices are made based on the validation set's performance, we need a third set to avoid selection bias and to report an unbiased estimate of the model performance. No perfect ratio can be determined a priori, but it should be determined based on the amount of available data. The basic rule is that the validation and test sets should be large enough to represent the whole range of variability found in real life. The remainder of the data can then be used for training. For a small amount of data, the ratio used for validation and testing will be relatively high compared to that used for training. However, as the amount of data grows, this ratio will become smaller ([PERSON] et al., 2018). Considering that we had a relatively medium-sized dataset, this study allocated 70% of our dataset for training, 25% for validation, and 5% (10 radiographs for each class) for testing.