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ObjectiveCurrent approaches for soft tissue thickness evaluation and visualization still represent a challenge for full extent evaluation and visualization. The aim of this clinical technique article is to introduce a novel approach for comprehensive visualization and precise evaluation of oral soft tissue thickness utilizing a fusion of optical 3D and cone‐beam computed tomography (CBCT) images.Clinical considerations3D models of the maxilla were obtained by CBCT imaging and intraoral scanning. The CBCT images were reconstructed to standard tessellation language (STL) file format models by segmentation of teeth and bone using implants planning software. 3D soft tissues and teeth models were obtained by intraoral scanning and were exported in STL file format as well. 3D multimodal models were then superimposed using best‐fit matching on teeth. Soft tissue thickness was then visualized and evaluated with a 3D color‐coded thickness map of gingival and palatal areas created by surface comparison of both 3D models. Additionally, threshold color‐coding was used to increase comprehensibility. Palatal areas were further visualized and evaluated for the optimal donor site.ConclusionsA novel approach for 3D evaluation and visualization of masticatory mucosa thickness presents all available 3D data in a comprehensible, “clinician‐friendly” manner, using threshold regions and clinically relevant views.Clinical significanceProposed approach could provide comprehensive presurgical treatment planning in periodontal plastic surgery and implantology without additional invasive procedures for the patient, resulting in more predictable treatment, improved outcomes, and reduced risk for complications.

Aim This study aimed to determine the optimal reference area for superimposition of serial 3D dental models of patients with advanced periodontitis. Materials and Methods Ten pre‐ and post‐periodontal treatment 3D models (median time lapse: 13.1 months) of patients with advanced periodontitis were acquired by intraoral scanning. Superimposition was performed with the iterative closest point algorithm using four reference areas: (A) all stable teeth, (B) all teeth, (C) third palatal rugae and (D) the whole model. The superimposition accuracy was evaluated at two stable evaluation regions using the mean absolute distance and evaluated with two‐way ANOVA and post‐hoc multivariate model. The intra‐ and inter‐operator reproducibility was calculated by intraclass correlation coefficient (ICC). Results Superimposition accuracy evaluated at stable tooth evaluation region were 71 ± 29 μm, 73 ± 21 μm, 127 ± 52 μm and 113 ± 53 μm for areas A, B, C and D, respectively. All reference areas showed similarly high ICC values >0.990, except for reference area C showing ICC of 0.821 (intra‐operator) and 0.767 (inter‐operator) for tooth evaluation area. Conclusions Area A and B provide the highest accuracy for superimposition of serial 3D dental models acquired by intraoral scanning of patients with advanced periodontitis.