## 4 Discussion
The selection of the GOM-Atos Compact Scan 12M industrial scanner (Zeiss, Germany), with a precision deviation ranging from 3 to 12 \(\upmu\)m, as the reference system is supported by its high accuracy [18; 33; 34; 35]. Achieving accurate intraoral scans for full-arch implant-supported restorations presents considerable challenges. Inaccuracies in the definitive cast may lead to a misfit that cannot be compensated for by the absence of periodontal ligaments, potentially resulting in complications involving the implants or prosthesis, including screw loosening, ceramic fractures, implant failure, or peri-implant bone loss [36]. Potential sources of error include the design and material of the Scanbodies, implant angulation and position, a lack of anatomical landmarks, mobility of the surrounding mucosa, uniform tissue morphology, increased inter-implant distances, and operator experience [21; 36]. Additionally, cumulative errors, particularly in the mandible, can occur during image acquisition over larger scanning areas, further compromising accuracy [37]. To overcome some of these limitations, clinical reports have described the use of screwed optical markers in combination with photogrammetry to record the position of dental implants for fabricating implant-supported prostheses [26; 30; 38]. However, it is important to note that the photogrammetry exclusively captures the spatial positioning of implants. As a result, supplementary procedures, such as digital impressions with IOSs, are necessary to accurately record soft tissue morphology, requiring the integration of both datasets through superimposition [30]. Although photogrammetry is faster than both IOSs evaluated, the requirement for an additional step to digitize the soft tissue ultimately extends the overall workflow duration. Numerous techniques have been reported in the literature to evaluate the accuracy of scanning devices, including mean 3D deviations, linear and angular measurements without model superimposition, absolute distance evaluations, and axis-specific (X, Y, and Z) deviations based on best-fit alignment algorithms [39]. In the present study, data analysis was performed using a best-fit alignment approach, wherein trueness was assessed by superimposing the scan data onto a reference model and calculating of the root mean square (RMS) error value [24]. The RMS method, a continuous metric, was chosen for several reasons: (a) it quantifies deviations across all the dataset, offering a comprehensive trueness assessment; (b) it facilitates direct comparisons among various scanning technologies; (c) it incorporates all data points, thereby minimizing bias associated with the selection of specific landmarks or planes and reducing operator dependency compared to linear or angular methods; and (d) it is widely adopted in the literature, particularly in studies within this field [40]. Despite its advantages, the RMS approach has limitations: it does not elucidate the type or spatial distribution of errors, and its outcomes are highly dependent on the alignment algorithm used [6]. This methodological dependence can introduce variability and may hinder the detection of localized inaccuracies in the scanned dataset. No additional localization algorithm was applied in the present study. Instead, to complement the global RMS analysis, alignment was performed using a best-fit method based on the iterative closest point algorithm, ensuring consistent alignment across datasets. It is essential to note that the accuracy of an impression method is defined by two fundamental parameters: trueness, which refers to the closeness to a reference standard, and precision, which indicates the consistency of measurements across multiple repetitions.