## 4 Discussion
(2017) developed AI-based algorithms to predict patient age by analyzing the developmental status of third lower molars. Despite achieving only a 0.51 accuracy, they were able to demonstrate that misclassifications were most likely to occur between adjacent developmental stages [28]. The results indicated that only slight differences in tooth morphology could be difficult to distinguish when using small datasets. According to the BoVW approach, we found that other foreign materials in the panoramic radiography, such as endodontic fillings, implants, etc., can impair diagnostic accuracy. [PERSON] et al. alsodemonstrated that practitioners with 5 to 10 years of experience were capable of achieving results similar to the AI algorithms [28]. Our previous study on the prediction of OAC in OPGs was able to confirm this observation [16]. [PERSON] et al. developed similar protocols to predict the existence of inflamed maxillary sinuses. In this study, they achieved an AUC of 0.93 and 0.88 for the two datasets, as well as 0.83 and 0.89 for the practitioners, with a [PERSON]'s kappa coefficient of 0.82 compared to practitioners [29]. Using multilayer perception models (MLP) and radial basis function neural networks (RBNN), we were able to obtain comparable results in predicting OAC. The Archer classification yielded an AUC of 0.59, whereas the RS classification and the use of RBNN led to a slightly higher AUC of 0.63. However, both values indicate no satisfactory predictive ability. A study published recently found an average accuracy of less than 90% for the use of artificial intelligence in dentistry-related tasks. This may result from the fact that panoramic radiographs contain a great deal of information and may be variable compared to chest X-rays (i.e., various numbers of teeth, fillings, foreign metallic objects) [30]. An example of the popular usage of a large number of open-source database images would be the detection of COVID-19 or pneumonia patterns in 2D chest X-rays. Various studies have demonstrated an accuracy range of 0.829 to 0.990 in solving the binary classification task [31]. There are strengths and limitations associated with this study. Overall, these are the following innovations that we could provide with this work: (1) To the best of our knowledge, this is the first study to determine the diagnostic metrics of the included classification systems utilizing artificial intelligence-based approaches to panoramic radiography. (2) We have included several suitable methods compared to other studies working on different topics in dentistry, allowing others to assess differences in metrics for panoramic radiography classification tasks. Methods can be easily adapted for other classification tasks (e.g., assessing the relationship between lower molars and the alveolar nerve). Our dataset and algorithms can be utilized as a fundament for other AI-based algorithms, statistical models, or other evaluations. (3) It is our follow-up study that evaluates a highly relevant clinical topic: the preoperative prediction of OAC from panoramic radiography. Our work group is the first to investigate this task from panoramic radiography with advanced AI-based methods, to the best of our knowledge. Considering the high number of upper teeth extractions in dentistry and the high prevalence of OAC, our work could have great clinical value. OAC can lead to serious complications for patients. In case more authors are working on this topic and build on the fundament we provided with this work, it might be possible to improve the provided models in the future and introduce a preoperative risk stratification algorithm before tooth extraction. This would be a great step toward precision medicine/dentistry. There are, however, some limitations due to the retrospective nature of the data. The small openings of the maxillary sinuses are not likely to require any intervention under healthy conditions, such that the data may be biased as an unreported case [7].