Implant dentistry, like many other fields, benefited tremendously from the use and development of digital technology. The advantages offered by cone-beam computed tomography (CBCT) and intraoral digital impressions have secured them a critical position in dental implant treatment planning. Furthermore, the ability to combine Digital Imaging and Communications in Medicine (DICOM) and Standard Tessellation Language (STL) files using dedicated computer-aided design (CAD) software has revolutionized the use of digital workflow (Fig. 1) and static computer-assisted implant surgery (sCAIS). Allowing the clinical team to visualize and refine implant positions pre-surgically, using virtual treatment planning software, has improved early evaluation of implant positioning in relation to vital anatomical structures, hard and soft tissues, as well as the planned final prosthesis (Di Giacomo et al. 2005; Nickenig et al. 2007; Park et al. 2009; Hämmerle et al. 2009; Joda et al. 2017). 

However, the accurate transfer of the virtually planned implant position from the planning software to the patient’s mouth involves multiple technique-sensitive steps critical to reproducing the desired preoperatively planned and prosthetically driven result using sCAIS. Errors during data acquisition, data transfer, data processing, treatment planning, guide design, and production, as well as surgical execution, were found to contribute to increased deviation values (Fig. 2) from the virtually planned implant position (Cassetta, Stefanelli et al. 2013; Cassetta, Giansanti et al. 2013; Cassetta et al. 2015; Cassetta, Di Mambro, Giansanti, Stefanelli and Cavallini 2013; Cassetta, Di Mambro, Giansanti, Stefanelli, and Barbato 2013; Cassetta, Di Carlo et al. 2013; Schneider et al. 2015; Muller et al. 2016; El Kholy, Janner et al. 2019; El Kholy, Lazarin et al. 2019; El Kholy, Ebenzer et al. 2019).

The latest ITI Consensus Conference (Amsterdam 2018) acknowledged that, although there was an overall improvement in accuracy of sCAIS, there was also a wide variation in levels of evidence between studies conducted on static computer-assisted implant placement (Wismeijer et al. 2018). The consensus statement by the group on digital technology concluded that several factors within the digital workflow progression could contribute to deviations in the final implant position. Further, the group stressed the need for future research to investigate those factors separately.

It is clear that sCAIS, when successfully planned and executed, can result in a more accurate surgical positioning of the implant than freehand surgeries (Van Assche et al. 2012). This increase in accuracy can further optimize the prosthetically driven 3D positioning of the implant fixture (Zitzmann & Marinello 1999; Tzerbos et al. 2010; Baggi et al. 2013; Garber & Belser 1995). However, the predictability of consistently achieving a high degree of accuracy when using sCAIS has been a subject of debate. The effect of different variables, including variations between types of guide support, drilling distances, and implant specifications, is still poorly understood. Furthermore, with software and hardware emerging every year from multiple manufacturers, it is crucial to focus on the basic engineering concepts involved in the processing, planning, and execution of these procedures to be able to establish basic and universal principles that clinicians can apply to achieve a reproducible and predictable outcome. As the popularity of the sCAIS continues to increase, the responsible introduction of its broad application requires a comprehensive knowledge of standard variables and decision-making points during treatment planning and execution.

At the University of Bern, a series of in-vitro studies (El Kholy, Janner et al. 2019; El Kholy, Lazarin et al. 2019; El Kholy, Ebenzer et al. 2019) were undertaken to test the effect of some common variables shared by multiple software and guided surgery systems on the accuracy of sCAIS procedures. While the research group recognizes that clinical studies are essential to evaluate the clinical efficiency and efficacy of developed treatment protocols, the refinement of technology application often requires a return to lab-testing to isolate the numerous variables introduced along the journey to operational execution. For example, conducting a clinical trial to investigate the effect of guided sleeve height differences on sCAIS accuracy would be impractical due to the difficulty of applying firm standardization criteria to variables including different implant sizes, the difference in sleeve clearance heights, quality of bone at the implant site, implant macro design and presence or absence of simultaneous grafting procedures. Therefore, an in-vitro study model was chosen to enable the better isolation of different variables encountered during data transfer, from patient to software and vice versa, treatment planning, and surgical execution. The research team used a reverse engineering approach to analyze multiple possible variables encountered during the digital treatment planning and execution process of sCAIS procedures. The team identified three areas of potential variables involved in guide design and support, implant macrodesign and drilling distances. The team hypothesized that these variables could be better controlled to increase predictability and accuracy of sCAIS treatment. The following is a summary of a number of their significant findings.