Clinical Applicability of Artificial Intelligence–Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry‐Based Cohort Study

Abstract

OBJECTIVES: This study evaluated the accuracy, time efficiency, and workflow consistency of artificial intelligence (AI)-assisted versus human expert (HI) implant planning in the esthetic anterior maxilla. MATERIAL AND METHODS: Thirty-five single-tooth anterior maxillary cases with paired cone beam computed tomography (CBCT) and intraoral scans (IOS) were retrospectively recruited. A hybrid AI framework (Relu Automate), integrating rule-based constraints (≥ 2 mm labial bone, ≥ 1 mm palatal bone) with deep learning segmentation, was compared with conventional HI. The performance of each planning approach was evaluated based on the following: coronal and apical linear deviations, angular deviation, bone thickness, wax-up alignment, surgical guide fit, planning time, and consistency. Statistical analyses were performed with a significance level of 5% (α = 0.05). RESULTS: AI-assisted planning demonstrated clinically acceptable accuracy with mean coronal, apical, and angular deviations of 0.96 ± 0.6 mm, 1.24 ± 0.66 mm, and 4.3° ± 2.9°, respectively, meeting clinical thresholds in 94.3% (coronal), 85.7% (apical), and 94.3% (angular) of cases. Bone thickness measurements were equivalent between groups (labial: 2.0 mm for both groups; palatal: 1.5 mm AI versus 1.3 mm HI; p > 0.05). AI-assisted planning decreased the median planning time by 41.4% (p < 0.0001) while achieving perfect consistency compared to human experts (p < 0.05). ICC analysis revealed excellent agreement (≥ 0.9) across all spatial coordinates between AI and HI planning. CONCLUSIONS: AI-assisted implant planning achieved accuracy comparable to expert planning with improved efficiency and consistency, while AI-designed surgical guides demonstrated fit comparable to human-designed guides in the maxillary esthetic zone.