## 1 Introduction  
Lateral cephalometric radiography is a paracilical study that orthodontists routinely use in diagnosis, treatment planning, and treatment outcome assessment [1]. Correct craniofacial skeletal analysis is dependent on recognising and finding anatomical and structural landmarks. The centre of the sella turcica is a useful reference point for determining the location of the jaw in relation to the base of the skull [2]. Previously, orthodontists would trace lateral cephalometric analysis drawings by hand on translucent tracing paper, a laborious and time-consuming task. The quality of the procedure relies on the expertise of the person performing it, and this quality can vary between different orthodontists. This inconsistency has a substantial impact on the effectiveness and precision of clinical therapy [3]. Artificial intelligence (AI), a field of study within computer science, focuses on developing and studying algorithms that can handle both basic and complex tasks, often rendered as a software product [4]. Currently, the field of medicine predominantly utilises a subset of artificial intelligence known as machine learning (ML) and, more recently, a branch named deep learning (DL), specifically a subsection of artificial neural networks (ANN) named convolutional neural networks (CNN) [5; 6; 7]. The use of artificial intelligence (AI) has steadily increased in the fields of medicine and dentistry in recent years. It is applied in endodontics to diagnose dental decay, periapical lesions, fissures of the roots, the degree of difficulty of the case, the efficacy of retreatment, the morphology of the root canal system, and the working length. It has been used in robotic semi-automated surgery and image-guided surgery within the field of oral and maxillofacial surgery. AI is used in dental education to generate clinical outcomes and scenarios in order to create high-quality educational environments. It is utilised for treatment planning, result simulation, analysis of radiographs, pressure points for tooth movement, and 3D aligners in orthodontics [8]. In cephalometry, automated landmark identification was first documented in the mid-1980s, but the level of inaccuracy was too significant to be applied in clinical practice [9]. As technology advanced, lateral cephalometric analyses transitioned to digital platforms, resulting in notable enhancements in terms of speed, quality, and dependability [10]. The orthodontist may choose between manually identifying each cephalometric point or allowing the software to automatically identify the landmarks. Subsequently, the computer constructs the lines and calculates the measurement values for the angles associated with these landmarks. Most of the artificial intelligence algorithms implemented for automatic landmark tracing on lateral cephalometric radiographs exhibit a high level of accuracy. A significant number of studies reported a confidence interval within a range of 2 mm, with the mean percentage of accurately identified landmarks being over 80%. Nevertheless, in terms of clinical assessment, a localization error of up to 2 mm may be deemed acceptable for certain sites in a cephalometric examination, but not for all [11]. Precise localization of cephalometric sites A and B in the horizontal plane is essential for determining the maxillary and mandibular relations in the sagittal plane. The pituitary gland is situated in a saddle-shaped concavity in the body of the spine-noid bone called the sella turcica. It is limited anteriorly by the tuberculum sella and posteriorly by the dorsum sellae, which is a square-shaped bone. The posterior clinoid processes are two tubercles found at the upper end of the dorsum sellae [12; 13]. The anterior clinoid processes are part of the sphenoid bone's small wings. The sella turcica is surrounded by the anterior and posterior clinoid processes [14].