## 1 Introduction  
Diagnosing the need for orthognathic surgery is an important issue in the field of orthodontics and oral and maxillofacial surgery [1]. Orthognathic surgery is expensive and associated with the risks of general anesthesia. Therefore, patients tend to prefer orthodontic treatment to orthognathic surgery. However, there are cranial structural problems that cannot be solved by orthodontic treatment alone. A prominent chin, retrusive mandible, and jaw asymmetry can only be corrected by orthognathic surgery. Orthognathic surgery is also chosen when there is a limit to the esthetic improvement that can be achieved with orthodontic treatment alone [2, 3]. The choice of orthognathic surgery depends on the evaluation of the objective data and not just the patient's decision. The determination of whether a patient's skeletal problem can be solved by an orthodontic approach or if it requires a surgical approach is a very sensitive issue [4]. Experienced orthodontists and oral surgeons can evaluate the need of a case, but this can be difficult if the clinician is less experienced or if it is a borderline case. Several clinical data are selected for this differential diagnosis [5], of which cephalometric radiographs are the most important radiological data used to determine the antero-posterior skeletal problems of patients [6; 7]. For most surgical evaluations, determining whether a patient has a prominent jaw or retrusive jaw is most critical, and cephalometric radiographs are used to determine whether the difference between the maxilla and mandible can be overcome by orthodontic treatment alone or whether surgery is necessary. There are approximately 50 landmarks used in cephalometric radiographs, and various measurements such as line length and angle using these landmarks are used for analysis [8; 9]. Different clinicians may consider different measurements important and analyze experimentally in light of their professional experience. This could be a difficult process for less experienced clinicians, and it can be difficult to have confidence in their decisions. Furthermore, there is a reason why these measurements cannot be used as is. Even the sella-nasion-A point (SNA) angle and sella-nasion-B point (SNB) angle, which are the measurement values of the anterior and posterior positions of the maxilla and the mandible, may show different values even in the same skeletal state due to the change in the position of the nasion [10]. Therefore, in measurement-based evaluation, the supplementary decision of the clinician is important. Artificial intelligence (AI), especially deep learning using convolutional neural networks (CNNs), has become the most sought-after field [11; 12; 13; 14]. With the development of computing hardware and graphics processing units, the computational processing speed has increased, complex calculations can be performed in a short time, and deep learning using deep convolutional neural networks (DCNNs) has been activated [15; 16]. Particularly, the development of deep learning in the categorization of images is surprising, and in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) of 2015, it went beyond human perception [17]. Various deep learning neural network models are being developed and applied to many fields. Image pattern recognition and classification problems are practically used in all fields; hence, application of deep learning is endless [18]. The medical field is no exception, and deep learning is being applied to different issues, including diagnosis, in several clinical fields [19; 20]. There have been many studies using various AI models in orthodontics [21; 22]. When determining the need for extraction, there were studies that applied machine-learning models to predict the result by constructing neural networks using landmark measurements [23; 24]. These studies had the disadvantage of having to manually measure the landmark points, calculate the measurement values, and input them in the input stage in order to apply the neural network model.