# Reproducibility and Accuracy of Two Facial Scanners: A 3D In Vivo Study
## 1 Introduction  
In the medical field, including in orthodontics and maxillofacial surgery, the application of three-dimensional (3D) imaging techniques has significantly expanded in recent years [1]. Several technologies now enable the creation of three-dimensional (3D) facial models, using methods like computed tomography or cone beam computed tomography data, stereophotogrammetry, and laser or structured light scanning, replacing traditional 2D analysis and allowing for detailed measurement and comparison of surface areas, volumes, and angles [2, 3, 4]. The literature shows that 3D facial scanners are highly precise and accurate, making them ideal for dentistry [5, 6, 7], by improving the evaluation and planning of orthodontic treatments and enhancing communication between orthodontists and surgeons [8, 9, 10]. Particularly, 3D photogrammetry shows promise in orthodontic fields by allowing the analysis of frontal views with the ability to rotate, translate, and zoom, thereby facilitating realistic analyses and treatment outcome predictions [11]. Moreover, the development of these new acquisition technologies has made it possible to capture high-quality images useful for landmark detection, replacing cone beam computed tomography (CBCT). In this way, sequential frames can be obtained while limiting radiation exposure for the patient [12]. In a previous study, [PERSON] et al. compared the accuracy of three face scanners with different acquisition systems and showed that all proved to be effective in capturing 3D images of the face. Area overlaps analysis between the scanners confirmed the accuracy of all the systems, with more than 90% of each area analyzed falling within the reproducible band (from 1.5 to \(-1.5\) mm) [13]. However, the analysis of superimpositions conducted in the literature revealed the presence of areas that were more reproducible than others, with higher accuracy in the middle and lower regions of the face [14]. Indeed, the forehead is the least reproducible, with an average overlap percentage varying from 11% to 50% in the highly reproducible band (0.5 mm to 0 mm and 0 mm to \(-0.5\) mm). The right and left cheeks, instead, had the highest average value (almost 60%) of the surface in the highly reproducible category, followed by the tip of the nose and the chin (around 55%) [9; 13]. Three-dimensional (3D) photogrammetry is widely available through various imaging systems. Leveraging the safety of LED white light, numerous devices have emerged, varying in cost, capture methods, hardware, and software. These systems provide an efficient way to acquire high-resolution surface data quickly, making them particularly advantageous for young or developmentally impaired subjects [14]. There are two types of devices that utilize LED white light based on the method of data capture: stereophotogrammetry and structured light systems. Digital stereophotogrammetry stands out as one of the most frequently employed technologies for three-dimensional (3D) facial surface imaging. However, the structured light system method also provides high-resolution 3D geometry measurements together with a relatively high measurement speed and accuracy [15]. In the last few years, the use of smartphones in conjunction with artificial intelligence has become commonplace in orthodontics, allowing the clinician to benefit from their accessibility, affordability, and practicality. Consequently, 3D imaging systems for smartphones are now widely used in medical and health fields due to improved camera capabilities. However, what has emerged is that the average accuracy of smartphones is lower (from 0.460 to 1.400 mm) compared to that of stationary stereophotogrammetry devices (from 0.087 to 0.860 mm) and portable scanners (from 0.150 to 0.849 mm) [16]. Thus, given the importance of 3D cephalometric areas and points analysis in orthodontics, this study aimed to compare the accuracy and reproducibility of two 3D imaging devices and direct anthropometric measurements.