Full-mouth implant rehabilitation is a complex procedure requiring precise three-dimensional implant positioning to achieve functional, esthetic, and biomechanical success. Even minor deviations may lead to prosthetic misfit, occlusal overload, or peri-implant complications [1]. Static computer-guided implant surgery introduced a prosthetically driven approach, improving accuracy over freehand placement by controlling implant trajectory through pre-fabricated templates [2]. However, static systems are limited by lack of intraoperative flexibility, guide instability, and cumulative errors arising from scanning, planning, and manufacturing processes [3]. Dynamic navigation systems overcome these limitations by enabling real-time tracking of implant drills relative to patient anatomy, allowing intraoperative corrections [4]. Recent integration of artificial intelligence has further refined these systems through automated landmark recognition, adaptive planning, and error prediction algorithms [5-10]. Despite increasing adoption, limited clinical data exist comparing static, dynamic, and AI-assisted navigation specifically in full-mouth rehabilitation. This study was designed as an original clinical investigation to quantify accuracy differences among these modalities.

Study Design and Ethical Approval This prospective, comparative clinical study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committee. Written informed consent was obtained from all participants. Study Population Sixty adult patients (age range: 35–70 years) requiring full-mouth implant rehabilitation were enrolled. Inclusion criteria • Fully or partially edentulous arches requiring ≥6 implants • Adequate bone volume for implant placement • Good systemic health (ASA I–II) Exclusion criteria • Uncontrolled systemic disease • History of radiotherapy in head and neck region • Severe parafunctional habits Group Allocation Patients were equally allocated into three groups: • Group I: Static surgical guide • Group II: Dynamic navigation • Group III: AI-assisted dynamic navigation A total of 360 implants (120 per group) were placed. Surgical Protocol Preoperative CBCT scans were obtained and prosthetically driven implant planning was performed. Implant placement was carried out by the same experienced surgeon to minimize operator bias. Accuracy Assessment Postoperative CBCT scans were superimposed on preoperative plans using dedicated software. The following parameters were measured: • Coronal deviation (mm) • Apical deviation (mm) • Angular deviation (degrees) Statistical Analysis Data were analyzed using SPSS v25. One-way ANOVA with Tukey post-hoc tests was applied. Statistical significance was set at p < 0.05.

Table 1 (Coronal Deviation) Table 1 demonstrated a progressive reduction in coronal deviation from static guided surgery to AI-assisted dynamic navigation. The static guide group showed the highest mean coronal deviation (1.32 ± 0.34 mm), indicating greater discrepancy between planned and achieved implant positions. Dynamic navigation significantly improved coronal accuracy, with a mean deviation of 0.91 ± 0.26 mm. The lowest coronal deviation was observed in the AI-assisted navigation group (0.62 ± 0.18 mm). Statistical analysis revealed a highly significant difference among the three groups (p < 0.001), confirming that real-time navigation and AI-driven adjustments substantially enhanced coronal precision during full-mouth implant placement. Table 2 (Apical Deviation) As shown in Table 2, apical deviation followed a similar accuracy trend across the study groups. Implants placed using static surgical guides exhibited the highest apical deviation (1.76 ± 0.42 mm), reflecting cumulative errors related to guide seating and drilling tolerance. Dynamic navigation reduced apical deviation to 1.14 ± 0.31 mm, demonstrating improved control of implant trajectory at deeper osteotomy levels. The AI-assisted navigation group achieved the greatest apical accuracy, with a mean deviation of 0.79 ± 0.22 mm. Intergroup comparison revealed statistically significant differences (p < 0.001), emphasizing the advantage of AI-enhanced real-time feedback in minimizing deep apical positioning errors in full-arch rehabilitations. Table 3 (Angular Deviation) Table 3 highlighted marked differences in angular deviation between the three implant placement modalities. The static guide group recorded the highest angular deviation (4.8° ± 1.2°), which is clinically relevant in full-mouth rehabilitations where angular discrepancies may compromise prosthetic passivity. Dynamic navigation significantly reduced angular deviation to 3.1° ± 0.9°. The AI-assisted navigation group demonstrated the lowest angular deviation (2.1° ± 0.6°), indicating superior control over implant angulation. The reduction in angular deviation across groups was statistically significant (p < 0.001), underscoring the role of AI-based predictive correction in enhancing prosthetically driven implant alignment. Table 4 (Surgical Time) Table 4 compared the mean surgical time per implant across the three groups. The static guide group required the longest surgical time (14.6 ± 3.2 minutes per implant), largely due to guide positioning and verification steps. Dynamic navigation reduced operative time to 13.1 ± 2.8 minutes, reflecting improved workflow efficiency. The AI-assisted navigation group demonstrated the shortest surgical time (10.9 ± 2.4 minutes), with a statistically significant reduction compared to both other groups (p < 0.01). This finding suggests that AI-driven automation and real-time guidance not only improve accuracy but also enhance procedural efficiency in complex full-mouth implant rehabilitations. Table 1. Coronal Deviation Across Groups Group Mean ± SD (mm) Static guide 1.32 ± 0.34 Dynamic navigation 0.91 ± 0.26 AI-assisted navigation 0.62 ± 0.18 Significant reduction in coronal deviation was observed with AI-assisted navigation compared to other groups (p < 0.001). Table 2. Apical Deviation Across Groups Group Mean ± SD (mm) Static guide 1.76 ± 0.42 Dynamic navigation 1.14 ± 0.31 AI-assisted navigation 0.79 ± 0.22 Apical deviation followed a similar trend, with AI-assisted systems demonstrating superior accuracy (p < 0.001). Table 3. Angular Deviation Across Groups Group Mean ± SD (degrees) Static guide 4.8 ± 1.2 Dynamic navigation 3.1 ± 0.9 AI-assisted navigation 2.1 ± 0.6 Angular deviation was significantly lower in Group III (p < 0.001). Table 4. Surgical Time Comparison Group Mean time per implant (minutes) Static guide 14.6 ± 3.2 Dynamic navigation 13.1 ± 2.8 AI-assisted navigation 10.9 ± 2.4 AI-assisted navigation significantly reduced surgical time (p < 0.01).

The present original research investigated the evolution of implant placement accuracy in full-mouth rehabilitation by comparing static surgical guides, dynamic navigation, and AI-assisted dynamic navigation systems. The findings demonstrated a clear, statistically significant improvement in linear and angular accuracy parameters as implant guidance evolved toward real-time and AI-integrated technologies. These results confirm that technological sophistication plays a decisive role in minimizing placement deviations in complex full-arch implant therapy [1,2]. Static guided surgery exhibited the highest coronal, apical, and angular deviations. Although static guides are known to enhance accuracy compared to freehand techniques, their limitations are well documented. Errors related to CBCT acquisition, guide fabrication, guide seating, and drill tolerance may accumulate and become magnified when multiple implants are placed in full-mouth rehabilitations [3,4]. The higher angular deviation observed with static guides is particularly concerning, as angular discrepancies have been associated with prosthetic misfit, increased strain on superstructures, and higher risk of mechanical complications in full-arch restorations [5]. Dynamic navigation significantly improved accuracy outcomes compared to static guides, as evidenced by reduced coronal and apical deviations and improved angulation control. The real-time visualization of drill position relative to the planned implant trajectory allowed intraoperative correction of deviations, which is especially advantageous in edentulous arches with limited anatomical landmarks [6,7]. These findings are consistent with contemporary studies reporting superior spatial control and flexibility with dynamic navigation systems in full-arch and complex implant cases [8,9]. The AI-assisted dynamic navigation group demonstrated the highest accuracy across all measured parameters. The substantial reduction in angular deviation observed in this group is clinically significant, as precise angulation is essential for achieving passive prosthetic fit and long-term biomechanical stability in full-mouth implant rehabilitations [10,11]. The improved accuracy may be attributed to AI-driven automation of planning steps, enhanced landmark recognition, and predictive correction of drill trajectory during osteotomy preparation [12,13]. In addition to accuracy, surgical efficiency improved significantly with AI-assisted navigation. The reduced surgical time per implant observed in this group suggests that AI integration streamlines workflow by minimizing manual adjustments and reducing intraoperative decision-making burden [14]. Improved efficiency has direct clinical relevance, as shorter surgical duration may reduce patient discomfort, operative stress, and cumulative procedural fatigue, particularly in full-arch implant surgeries [15]. The present findings align with recent clinical and experimental investigations demonstrating that AI-enhanced navigation systems outperform conventional static and dynamic approaches in both accuracy and workflow efficiency [16,17]. However, the study had certain limitations. Implant placement was performed by a single experienced operator, and outcomes may differ with less experienced clinicians. Furthermore, the study focused on positional accuracy and did not evaluate long-term clinical outcomes such as implant survival, prosthetic complications, or peri-implant tissue response [18,19]. Despite these limitations, the results provide strong clinical evidence supporting the adoption of AI-assisted dynamic navigation for full-mouth implant rehabilitation. The progressive reduction in deviations from static guides to AI-driven systems highlights the transformative impact of artificial intelligence in digital implant dentistry. Future longitudinal studies correlating placement accuracy with long-term biological and prosthetic outcomes are warranted to further validate these findings [20,21].

AI-assisted dynamic navigation significantly improves implant placement accuracy and efficiency in full-mouth rehabilitation compared to static and conventional dynamic techniques. Its integration into digital implant workflows represents a substantial advancement in precision-driven implant dentistry.

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