## 4 Discussion
have recently proposed a whole-stage data augmentation method that increases the accuracy of deep convolutional neural networks [54]. This method can also be used to generate an implicit ensemble of models without incurring additional training costs. Due to the fact that this framework is widely applicable to all network architectures and techniques for the augmentation of data, it can be used for a wide range of deep learning-based activities. [PERSON] et al. explored the possibility of diagnosing diseases from uncontrolled 2D facial photographs using deep learning algorithms [55]. As in our study, these researchers incorporated transfer learning into their process. By initializing the weight, they replaced the last fully connected layer of the pre-trained CNN. When optimizing their CNN, the authors used forward propagation to derive activation value from the convolutional layer. In place of manual grid search, they used code-based hyperparameter tuning (random grid search) as a regularization technique. Both have numerous applications in the medical industry [20]. Another technique to optimize the outcome in prediction and classification tasks in the medical field is the combination of several data inputs (e.g., imaging, clinical data, genetic data, etc.) in the form of multi-input mixed data hybrid models [56]. Overall, in the present work, we sought to apply a wide range of regularization (e.g., data augmentation, EarlyStopping, learning rate decay) and optimization (hyperparameter tuning, manual layer engineering) techniques for model development. In our pre-studies, we also applied different ROIs to examine the effect of ROI determination on metrics and chose the ROI with the most representative region in panoramic radiography for upper molar region classification tasks. A general limitation is the artefacts that may be present in X-ray images, such as osteosynthesis plates, metal restorations, and other affecting factors such as patient positioning that can be a potential source of interference. We have considered these points in the data analysis [57]. [PERSON] et al. demonstrated that the topographical position of root apexes determined by panoramic radiography was significantly different than that determined by a CT image [38]. [PERSON] et al. describe similar results for the projection of the root tip into the maxillary sinus [37]. This might be a consequence of the representation of 3D structures on 2D images. In light of these factors, as well as others, several authors recommend the use of 3D imaging to provide additional information regarding the prediction of OAC, although they cannot be recommended generally due to radiation exposure and costs [11; 37; 58; 59]. As a whole, the applicability of the introduced algorithms should be investigated in more detail with a larger number of cases [30]. In the present study, we found superior predictability by considering classification systems rather than solely examining OPCs to predict OAC. Nevertheless, the diagnostic measures are still not suitable for clinical use. By including the classification system in future algorithms, it may be possible to reduce the number of images necessary to train a reliable OAC prediction algorithm. Finally, external validation from large-scale prospective studies is warranted to validate the provided results. In a previous study, we sought to compare experienced practitioners and AI-based approaches in predicting OAC from panoramic radiographs [16]. Our results revealed that the agreement among the practitioners varied greatly. The metrics of the seven different AI algorithms investigated showed substantial differences. However, in direct comparison, similar results were achieved for the AI and the practitioner predictions of OAC from panoramic radiographs. In conclusion, we were not able to predict OAC reliably, neither by the practitioners nor by an AI-based approach utilizing features from panoramic radiographs.