No abstract

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.

Aim To introduce and validate a computer‐aided method for direct measurements and visualization of gingival margin (GM) changes. Materials and Methods The method consists of five main steps: digital model acquisition, superimposition, computer‐aided GM detection, distance calculation between the GM curves, and visualization. The precision of the method was evaluated with repeatability and reproducibility analysis (n = 78 teeth). The method's repeatability was evaluated by repeating the algorithm on the same digital models by two operators. The reproducibility was evaluated by repeating the algorithm on two consecutive digital models obtained with a scan−rescan process at the same time point on the same patient. For demonstration, the proposed method for direct measurements of GM changes was performed on patients who had undergone root coverage procedures and treatment of periodontal disease. Results Excellent repeatability was found for both intra‐ and inter‐operator variability, that is, 0.00 mm, regarding computer‐aided GM detection. The reproducibility of computer‐aided GM detection evaluated on scan−rescan models was 0.10 mm. Conclusions The presented method enables the evaluation of GM changes in a simple, precise, and comprehensive manner through non‐invasive acquisition and superimposition of digital models.

No abstract

Background The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability. Methods Measurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland–Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots. Results Bland–Altman plots revealed a high variability of examiner’s recession measurements indicated by high 95% limits of agreement range of approximately 1 mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner’s variability due to smaller curvature values than GM, i.e., median values of − 0.98 mm− 1 and − 4.39 mm− 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1 mm− 1. Conclusions Automated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies.

No abstract

No abstract