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Automating virtual dental implant planning: can artificial intelligence match clinical expertise?
Bahaaeldeen M Elgarba1,2,3, Eslam Abdelwahab Dawood1,2,3, Rocharles Cavalcante Fontenele1,3,4
1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven 3000, Belgium.
Objectives:
This study evaluated a validated artificial intelligence (AI) tool for virtual implant planning by comparing its performance with expert-planned and clinically placed implants in single-tooth rehabilitation cases.
Methods:
Pre- and postoperative cone-beam CT (CBCT) scans were retrospectively collected from 32 single-tooth implant cases. Actual implants were placed by expert clinicians using a human intelligence (HI)-based approach guided by the preoperative CBCT. The same scans were used to generate both AI- and HI-based virtual implant plans. Pre- and postoperative scans were registered to allow accurate comparison between placed and planned implants. Three plans were evaluated per case: clinically placed implant (HI-place), HI-planned implant (HI-plan), and AI-planned implant (AI). Evaluations included angular deviation relative to adjacent teeth and prosthetic wax-ups, vertical and horizontal bone thickness, proximity to vital structures, implant dimensions, planning time, and planning repeatability.
Results:
The Friedman test indicated no significant differences in implant location among the 3 approaches (P > .05). Angular deviations were similar across methods for both the neighboring tooth (HI-place = 7.01 ± 4.7°; HI-plan = 7.30 ± 5.6°; AI = 6.86 ± 3.7°) and the wax-up (HI-place = 5.22 ± 3.0°; HI-plan = 4.55 ± 3.0°; AI = 5.50 ± 4.9°). Implant diameter and length selections also did not differ significantly across HI-place (4.06 ± 0.4 mm; 10.61 ± 1.4 mm), HI-plan (4.06 ± 0.4 mm; 10.45 ± 1.4 mm), and AI (4.05 ± 0.5 mm; 10.39 ± 1.4 mm). AI was significantly faster (P < .05), requiring 150 s vs 429 s for HI-plan, and showed excellent repeatability with zero deviation between repeated plans.
Conclusions:
AI-driven virtual implant planning achieved clinically acceptable placement comparable to expert clinicians in single-implant rehabilitation, with superior efficiency and repeatability, supporting its use in routine presurgical workflows.
Advances In Knowledge:
Integrating AI for implant location and dimensions selection, with accuracy comparable to expert clinicians, can streamline the digital implant workflow, reduce planning time, and allow clinicians to focus on verifying the AI-generated plan prior to clinical transfer, thereby increasing the time available for the actual treatment procedure.
