## 1 Introduction  
Permanent maxillary canines are, after third molars, the teeth most frequently impacted, with a prevalence ranging between 0.92% and 3% in the general population [1, 2, 3]. These teeth play a crucial role in dental esthetics and function, contributing to the smile arc, upper lip support, and posterior disclusion during lateral mandibular movements [4]. Maximillary canines follow the longest and most complex eruption path in the dentition [5], typically appearing between the ages of 10 and 12 years [6], making them more susceptible to eruption deviations [3, 7, 8].  
Their impaction is more common in females than in males [9, 10, 11, 12, 13, 14, 15, 16], and palatal impaction is significantly more frequent than buccal, particularly in the maxilla [17, 16]. Unilateral impaction occurs more frequently than bilateral impaction [12, 13].  
The diagnosis is generally made after the expected eruption period has passed and is based on a combination of clinical and radiological findings [3, 18, 19, 20]. Clinically, an impacted canine is suspected when there is delayed eruption of the permanent canine or prolonged retention of the deciduous canine beyond 14-15 years of age, absence of the canine eminence, presence of a palatal bulge, or delayed eruption, distal inclination, or migration of the lateral incisor [21].  
Radiological diagnosis is achieved through traditional methods such as panoramic radiographs [3,18,20,21], occlusal and periapical films [3,21], lateral cephalograms [22], and more recently, cone beam computed tomography (CBCT) [23]. Despite the higher diagnostic value of 3D imaging, panoramic radiographs remain the most commonly used due to their accessibility, lower cost, and reduced radiation exposure [18].  
Numerous studies have proposed diagnostic protocols analyzing the anatomical position of the impacted tooth, its relation to adjacent teeth, and the surrounding structures [3,22,23,24] using both 2D and 3D imaging techniques. In recent years, artificial intelligence-based prediction models have been proposed to improve the accuracy and early detection of canine impaction [25].  
The etiology of impacted canines is multifactorial [26,27], involving both genetic and environmental factors. Commonly associated dental anomalies include agenesis [28,29,30,31,32,33], the presence of other impacted or supernumerary teeth [32], lateral incisor size or shape anomalies [10,11,26,27,30,31,33,34,35,36], enamel hypoplasia [32], tooth transposition [30,32,37], lack of space or crowding [27], excessive space [29,38], disruptions in the eruptive pathway [8,26,39], or delayed dental development [29,40]. A genetic basis has been strongly suggested for many of these anomalies [9,27,31,35,37,41]. Additionally, associations have also been reported between canine impaction and skeletal dysplasia, specific malocclusion patterns [42,43,44,45,17,42,46,15], and even with craniofacial growth patterns [1,9,15,17,43,46].  
Despite extensive research, the current literature reflects low methodological quality, often with significant risks of bias [47]. Moreover, there is still no clear consensus regarding the primary etiological factors, which hinders the identification of reliable predictors and the implementation of effective preventive or interceptive strategies [48]. Therefore, further data collection is required to develop reliable predictive models based on patients' dental, skeletal, and occlusal characteristics, allowing the identification of canine impaction probabilities.  
The present study aims to analyze the prevalence of palatally displaced canines (PDC) and explore the dental and skeletal factors associated with PDC, thereby contributing to the evidence base for future diagnostic and therapeutic approaches.