ImplantPlanNet: A deep learning framework for automatic implant planning from preoperative CBCT images

Preoperative implant planning based on cone-beam computed tomography (CBCT) images supports prosthetically driven treatment but remains time-consuming and experience-dependent. This study developed and evaluated ImplantPlanNet, an automatic initial implant planning framework for single-tooth missing scenarios.

ImplantPlanNet incorporates candidate localization, local patch extraction, pose estimation from local patches, and geometric parameter recovery to estimate implant position, implant long-axis direction, length, and diameter. A dataset of 144 preoperative CBCT images from single-tooth missing sites was divided into training (n=104), internal testing (n=20), and external testing (n=20) sets. ImplantPlanNet-predicted implant plans were compared with specialist reference implant plans using three-dimensional (3D) coronal deviation, 3D apical deviation, angular deviation, dimension classification accuracy, and safety-related distance measurements.

Internal 3D coronal and 3D apical deviations were 1.53 ± 0.80 mm and 1.77 ± 0.82 mm, respectively, with an angular deviation of 5.55 ± 3.39°. Corresponding external values were 1.67 ± 1.74 mm, 2.21 ± 1.66 mm, and 6.92 ± 3.41°. Length classification accuracy was 65.0% in both sets; diameter classification accuracy was 100.0% internally and 75.0% externally. Safety-related distance measurements were generally comparable between ImplantPlanNet-predicted and reference implant plans, except for a slight reduction in buccal bone plate thickness in the external testing set.

The findings support the feasibility of using ImplantPlanNet to generate automatic initial implant plans from preoperative CBCT images for clinician review in single-tooth missing scenarios.

ImplantPlanNet may support clinician-supervised CBCT-based initial implant planning by generating proposals for single-tooth missing scenarios.