## 1 Introduction  
Standardizing a treatment to a high level of reliability and effectiveness, beyond personal human experience and capabilities, is the goal of applied Digital Science [1]. Computerized aided implantology (CAI), both static (s-CAI) and dynamic (d-CAI), allowed a more precise fixture placement than a freehand approach, reducing the surgical timing and the patient discomfort [2, 3, 4, 5, 6].  
This procedure, however, can present criticisms due to its complexity and the number of steps as well as the competence required. The quality of the Digital Imaging and Communications in Medicine (DICOM) from cone-beam CT (CBCT) and Standard Tesselation Language (STL) data and the reliability of the digital algorithm in merging these files condition the virtual planning [7, 8, 9, 10]. Furthermore, in s-CAI, the computer-aided manufacturing (CAM) process [11] and the structural features of the surgical guide are relevant to the final result [2, 4, 12].  
Assuming the integrity of the digital planning and guide printing, it appears helpful to verify whether, and how much, the personal operator's ability can interfere in the final surgical phase, improving the precision of the implant placement and shortening the surgical timing.  
Conflicting outcomes have emerged in in vitro and in vivo trials about the relevance of human expertise, not only as a freehand implantologist, but also whether there is competence in using s-CAI, and whether there is a learning curve. No relevance of the s-CAI expertise for skilled implantologists has been recorded [13], and better accuracy by novices under senior control has emerged compared to expert operators [14]. Significant deviations from the planned and the actual positions have been attributed to improper use of the handle piece, the conditioning of the action of the drills in the sleeves [15], and to the positioning of the guide into the patient's mouth [13]. The presence of improvement over time for experienced implantologists has been excluded, at least for edentulous patients [16]. In the most recent literature reviews on s- CAI, the operator's experience has not emerged as influencing the accuracy of the procedure. Nevertheless, the variability and unreliability of the assessment methods adopted by the studies considered were highlighted, due to the difficulty in performing meta-analyses [6; 7; 12; 17].  
Concerning the methods of accuracy evaluation, the degree of digital superimposition for the postoperative with the virtually planned implant positions--the matching of the pre- and post-CT DICOM data--has been ratified and shared as an accuracy parameter in the literature. The shifting of the coronal and apical portion of the fixture and its tilting and deepening were considered geometrical reference points [18]. Optical scanners introduced an alternative to the previous analysis, but the same factors affecting the registration process in the planning phase with these tools also conditioned the assessment method [19; 20; 21; 22]. No study has investigated the influence of operator skill on reducing the operative timing.  
The primary objective of the present study was to understand the relevance of the operator's expertise in conditioning implant placement accuracy with the s-CAI approach and to determine if accuracy improved over time for experts and novices (learning curve).  
The secondary objective was to understand if the operator's expertise could influence the operative time in the s-CAI approach.  
Three methods of superimposition data evaluation were adopted and compared to each other: CBCT (pre and post), CBCT (pre) and laboratory scanning (post), and CBCT (pre) and intraoral scanning (post).