## 4 Discussion
We carefully reviewed the operative reports in the data collection process to see if an OAC could be clinically confirmed. Conversely, it must be considered that the control group included cases in which no OAC was documented. If an OAC was erroneously not documented, a potential misclassification could have taken place. A more accurate data review would be possible in the context of a prospective study in which a standardized clinical review for an OAC is established. In addition, it might be possible to include CT imaging to assess small OACs in a standardized way in future studies and compare the results with 2D imaging. However, it might be complicated to obtain sufficient sample sizes, as CT imaging is not regularly performed for upper molar tooth extraction. Another potential future improvement could be automatic ROI detection by algorithms. This requires large datasets and separate algorithms detecting the ROIs before further processing. We are currently annotating a large dataset for the automatized detection of the sinus area with a subsequent automatized assessment of the marginal bone loss (unpublished data). The combination of these algorithms could further advance the efforts toward AI-based diagnostic approaches in oral and maxillofacial surgery. Furthermore, we have strictly divided the data into a train, test, and validation dataset, resulting in a smaller number of data for the learning process of the algorithms [32; 33]. A limitation of the current study is the small number of data collected and low occurrence of OACs given the large number of data required with the variability of panoramic radiographs [34]. In addition, the manual preparation of the images requires the identification of a region of interest, resulting in variations among studies [28]. Additionally, the process of labeling the region of interest by the algorithm is another potential source of error that requires training with large datasets before such algorithms can be used in clinics [30; 35; 36]. Despite achieving the goal of defining a section, there might be additional disadvantages depending on the quality of the obtained panoramic radiographs, considering their two-dimensional nature [37; 38; 39]. There are still many incomprehensible aspects of deep learning theory that have to be addressed. Normally, it is difficult to improve deep learning models in a focused manner due to their interpretation challenges. Therefore, researchers must consider both generalization and optimization. Even deep CNNs using large training data still struggle with the \"overfitting\" problem, which limits their ability to generalize their results from the training set to unknown test data. As a result, a larger model is typically more accurate [40], but users must choose between accuracy and reasoning speed in practice. In panoramic radiography, noise can both affect how expressive elements are extracted and how implicit knowledge is mined. The difficulties described above make it difficult to apply it successfully in some unique situations, such as medical diagnosis jobs [41], where there are few training data [42]. As of today, a variety of approaches have been developed to overcome the \"overfitting\" problem with deep CNNs. In order to reduce the complexity of a network, regularization methods can be utilized. A few such methods are Hierarchical Guidance and Regularization (HGR) learning [43] and L2-regularization [44]. Another approach is the extension of the sample dataset by means of data augmentation techniques such as translation [45] and horizontal flipping [46], as well as noise disruption [47]. Furthermore, Dropout [48], DropConnect [49], and GoogleLeNet [50] utilize ensembles of classification nodes in order to reduce reliance on a single network. Other approaches are a few distinctive training techniques, such as carefully planned initiation [51], early stopping [52], and learning rate decay [53]. [PERSON] et al.