## 4 Discussion  
AI is increasingly used in complex decision-making, problem-solving, and object recognition tasks. Studies evaluating the use of AI in age estimation have shown that AI can be more successful than manual techniques [19]. Thus, this study focused on AI instead of manual methods and aimed to evaluate the performance of 16 different convolutional neural networks (CNNs) in predicting age and gender from panoramic radiographs in children and young adults. The networks tested included DarkNet-19, DarkNet-53, Inception-ResNet-v2, VGG-19, DenseNet-201, ResNet-50, GoogLeNet, VGG-16, SqueezeNet, ResNet-101, ResNet-18, ShuffleNet, MobileNet-v2, NasNet-Mobile, AlexNet, and Xception. The hypothesis of the study that at least one artificial intelligence network would achieve an accuracy greater than 90% in predicting age and gender was rejected. The highest accuracy rate observed for age prediction was 80.59%, while the highest accuracy rate for gender prediction was 87.94%. The use of AI in forensic medicine and dentistry has been explored using various CNNs, dental X-rays, MRIs, and photographs [20; 21; 22; 23; 24; 25; 26]. Panoramic radiographs are commonly used in routine diagnosis, treatment planning, and follow-up of treatments in the maxillofacial region. They are easily accessible imaging methods and are frequently preferred in age estimation studies using AI [20; 23; 24; 27; 28; 29]. For these reasons, panoramic radiographs obtained from patients in the past were also used for this study. An additional advantage of age estimation using AI, in addition to the shorter completion time compared to traditional methods [30], is that it can be used for age estimation even in cases where conventional methods are not applicable, such as when dealing with individuals who have dental caries, missing teeth, restorations, or root canal treatments. AI-based age estimation has no limitations in these cases [20]. As a result, the radiographs of individuals who had undergone dental treatments were not excluded from our study, and a considerable number of panoramic radiographs could be used in our analysis. This enabled a more comprehensive and inclusive approach to age estimation using AI, encompassing a diverse range of individuals, including those who had received dental treatments. In addition to the chosen network, the most crucial factors for the success of artificial intelligence models are the data sample size, data heterogeneity, and training epochs [31]. Considering that the replacement of primary teeth with permanent teeth and the development of jawbones occur dramatically between the ages of 5 and 21, patients within this age range were selected for the study to provide precise data for the AI. Furthermore, some laws differ depending on age, with ages 14, 18, and 21 generally considered to be legal thresholds [32]. [PERSON] et al. [24] used 1332 panoramic radiographs obtained from patients who were aged from 8 to 68 for their study, and they reported the mean absolute error (MAE) values as follows: 3.47 for EfficientNetB4, 4.35 for ResNet50V2, 4.22 for DenseNet-201, 3.44 for InceptionV3, 3.68 for MobileNetV2, and 10.46 for VGG16. They also modified the InceptionV3 architecture and achieved an MAE of 3.13. We obtained lower MAE values in our study group aged 5 to 21. The MAE values were 0.75 for DenseNet201, 1.32 for MobileNetV2, and 1.04 for VGG16. It is well known that input size has a significant impact on the success of machine learning. In our study, the use of a larger input size, along with our focus on a younger age range, may explain the observed difference. According to the study conducted by [PERSON] et al. [23] using 1429 panoramic radiographs and employing AlexNet and ResNet-101, they reported an accuracy of 95.80% for AlexNet and 92.60% for ResNet. In our study, however, we obtained lower accuracies of 60.29% for ResNet-101 and 47.35% for AlexNet.