## 1 Introduction  
Orthopantomography (OPT), or panoramic radiography, is a dental imaging technique that captures a wide-view X-ray of the oral region. It provides a comprehensive overview of the teeth, jawbones, and surrounding structures in a single image. Thanks to its wide availability, low radiation exposure, and low cost, orthopantomography (OPG) is a widely used dental imaging technique that provides a panoramic view of the upper and lower jaws.  
OPT offers several applications in dentistry: it allows dentists to diagnose various dental conditions and abnormalities, it helps in identifying dental caries (cavities), periodontal disease, impacted teeth, cysts, tumors, and fractures, and provides a panoramic view of the entire dentition, enabling dentists to plan dental treatments, such as the optimal placement of dental implants, assessing bone density and quality, and evaluating the presence of any anatomical structures that may affect the treatment outcome. OPT also allows the workup of orthodontic therapy through the assessment of dental and skeletal relationships, crowding investigation, and the identification of missing or extra teeth [1; 2; 3]. Moreover, OPTs are routinely used to assess bone age [4] and for forensic purposes [5].  
Although OPT is a valuable imaging technique and provides clinicians with valuable diagnostic information, decision-making capabilities, and planning insights, this imaging technique has some limitations. The absence of three-dimensionality, potential artifacts, overlapping structure, and regional inhomogeneity may hinder an accurate interpretation by clinicians. Moreover, OPT images are subject to distortion and magnification, which can affect the accuracy of measurements. The positioning of the patient's head, the size and shape of the jaws, and anatomical variations can lead to image distortion and alter the true dimensions of the structures being examined [6; 7; 8].  
Teeth segmentation in OPT is the process of outlining and delineating the boundaries of individual teeth on the panoramic radiograph manually to extract specific tooth information from the overall image, which can be useful for various dental applications, such as treatment planning, analysis, and research [9].  
Manual segmentation is a meticulous and time-consuming process, requiring expertise in dental anatomy and familiarity with the imaging software, and it is often limited by high inter-operator variability. Introducing an automated assistance system could mitigate interrater variability and facilitate more reliable and precise evaluations of panoramic radiographs, particularly for less experienced professionals while saving time and improving efficiency [9].  
In recent years, artificial intelligence--particularly deep learning--has gained traction in computer-aided detection and diagnostics (CAD) [10; 11]. Deep learning models, primarily convolutional neural networks (CNNs), are multi-layered networks that transform input data (e.g., images) into outputs (e.g., presence/absence of disease) while progressively learning higher-level features. The notion of \"deep\" pertains to the multitude of layers used to extract features from input data [10; 12]. Deep learning models have demonstrated comparable performance to seasoned clinicians in specific tasks such as skin cancer detection, diabetic retinopathy diagnosis, lung cancer, and tuberculosis identification [10; 13; 14], resulting in a useful tool in an emergency setting [15].  
Within dentistry, a limited number of studies have investigated the potential of CNNs for automatically detecting or segmenting teeth on OPT(s) [6; 8; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Nonetheless, none have delved into automated multi-class detection, segmentation, and labelling on OPT(s). The objective of this study is to develop and validate the detection, segmentation, and labelling system founded on deep learning principles to enhance and further automate diagnostics within dental and oral surgical practices.