## 5 Decision Support for Orthognathic Surgery  
Another example of the use of AI in orthodontics is the field of orthognathic surgery, in which the decision for or against a necessity for surgical intervention can be made. There is a multitude of factors that plays an important role in deciding whether orthognathic surgery is indicated. In cases where it is impossible to achieve adequate occlusion with conventional means, orthognathic surgery must be considered. Moreover, such a procedure may also be indicated if the patient's chief complaints cannot be resolved by orthodontic treatment alone, for example, in terms of extraoral aesthetic improvement. Deciding whether orthognathic surgery is needed or an orthodontic treatment aimed at a compromise is feasible is one of the most important decisions when establishing an orthodontic treatment plan, as both treatment options differ significantly in terms of anchorage and the required tooth movements. While dental compensation should be undone in a surgical approach in order to achieve an optimal surgical effect, the opposite treatment is required in the case of a non-surgical approach to achieve an adequate camouflage of the dysgnathia. Therefore, irreversible procedures, such as tooth extractions, must not be performed until orthognathic surgery can be ruled out with certainty [86; 87]. The decision on whether orthognathic surgery is indicated may vary between different practitioners, owing to differences in experience and preferences [88], and clinicians with limited experience often have difficulties in making such decisions [86; 89]. As no standardized criteria for decision-making regarding the need for orthognathic surgery are available, there are approaches that attempt to support clinicians through the use of AI algorithms [86; 89; 90]. Initial studies demonstrated promising results for this decision-making task as well. [PERSON] et al., used different convolutional neural networks as AI algorithms that were trained to predict if orthognathic surgery was necessary on the basis of lateral X-rays and achieved correct prediction rates between 91% and 94% for a set of test images [89]. [PERSON] et al., expanded the diagnostic basis for the training of their AI by simultaneously using both lateral and posteroanterior X-rays of each patient and were able to achieve correct predictions of 95% regarding the indication for surgery [90]. [PERSON] et al., used other parameters in addition to the lateral X-rays as well, such as the overjet or the profile type, so that this research group was likewise able to increase the number of correct predictions regarding the indication for orthognathic surgery to 96% [86]. The remarkable feature of the latter work was that the AI could predict not only the indication for orthognathic surgery but also the indication for premolar extraction with a success rate of about 91%. In this context, it should be highlighted that the indication for extraction in class II patients worked better with a success rate of 97% than that in class III patients, where the successful decision rate dropped to 88%. The limitations of existing studies are similar to those on the extraction decision: Important aspects influencing the practitioners' decisions on whether orthognathic surgery is indicated, e.g., mandibular asymmetries, the patient's chief complaints, as well as aesthetic aspects, were not considered and should therefore be taken into account in future studies [86; 90]. In addition, with the exception of the work by [PERSON] et al., the existing studies are based on the assessment of a single expert with its associated and previously discussed limitations herein. Considering the fact that all three studies mentioned above refer to the Korean population, the general applicability of the results to other samples is doubtful. Furthermore, details regarding the surgical procedure itself cannot be predicted at present.