## 4 Discussion  
The present study examined the automated classification of impacted teeth using two classification systems and various artificial intelligence-based algorithms (i.e., machine learning and deep learning techniques). Further, we evaluated the association between the included classification systems and OAC and attempted to predict OAC based on high-risk classes in these classification systems. An assessment of the likelihood of an OAC can optimize operational planning. Therefore, it may be possible to prevent the potentially serious complications that may arise from OACs. A modern precision medicine approach would utilize reliable medical risk stratification algorithms to identify patients at risk in order to plan the closure of an OAC through a suitable incision before the operation takes place. In addition, when anticipating an OAC based on new prediction models, a closer follow-up of those patients might be beneficial to tackle a future problem. This approach would require investigating the data patterns that are currently available. Panoramic radiographs are widely available and well suited for this task. The hidden data patterns within panoramic radiographs are currently being investigated for various diagnostic purposes. Nevertheless, there was a lack of evidence examining its usefulness in OAC diagnosis. There are two requirements for optimal healing after upper molar teeth extraction, namely the presence of an uninfected maxillary sinus and a wound closure that is free from tension, well vascularized, and has a sufficient area of apposition. A small opening in the OAC that is not infected at the time of surgery may heal spontaneously by depositing a blood clot [6,7,21]. Spontaneous healing may not be possible in the case of openings larger than 3-4 mm on average and a probing depth exceeding 5 mm [22,23]. A surgical approach, which involves immediate closure of an OAC, has been associated with success rates of up to 95 percent, whereas secondary occlusions have been associated with success rates as low as 67 percent [7,23,24]. There is a great deal of innovative research interest in the surgical field with regard to closing an OAC. In addition to buccal advancement flap, rotation flaps with buccal cheek fat, local flap plastics with gingival tissue or substitute materials, and various different local rotation flaps, new techniques such as plated rich fibrin, which induces increased wound healing, are increasingly being discussed in the literature [9,25,26]. As we have found in a prior study, we were unable to obtain satisfactory results when using panoramic radiographs to predict OAC [16]. The object of this study was to identify classification systems that were significantly associated with an increased probability of occurrence of OAC. As [PERSON] et al. (2020) described, the Archer classification types B, C, D, and RS III/IV are significantly more frequently associated with OAC than Archer type A and RS classification types I, II, and V [19]. Other authors have confirmed these findings [18,27]. We were able to confirm the previously mentioned significant associations between OAC and Archer as well as RS classification in our study, and we presented a clinical decision tree using the CHAID algorithm. Furthermore, we have used a number of pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), a custom CNN, and the Bag of Visual Words technique to solve a binary classification task. In examining the Archer classification, we were able to achieve accuracies of up to 0.771 for EfficientNet and MobileNetV2, respectively. In terms of AUC, our custom-made CNN achieved values up to 0.902. Comparatively, the detection of the RS classification had an accuracy of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Considering all algorithms, the Archer classification was detected more reliably than the RS classification. [PERSON] et al.