The precision of gingival recession measurements is increased by an automated curvature analysis method

Spontaneous Recession Repair after Orthodontic Treatment: Case Report with the Use of Digital Approach for Quantification of Soft Tissue Changes

The article presents a case of spontaneous recession repair in a male patient with Class II malocclusion, division 1, after orthodontic treatment with aligners. The difference in digital recession depth was measured before and at the end of treatment by means of automatic intraoral scans superimposition within adapted software while using "Cross section" and "Measuring" instruments. Digital analysis of intraoral scans obtained before and at the end of treatment has revealed that recessions within the area of teeth 1.5, 1.4, 1.3, 1.2, 1.1, 2.1, 2.2, 2.3, 2.4, and 2.5 have improved, and recession depth reduced by 0.73 ± 0.08 mm, 1.02 ± 0.09 mm, 1.86 ± 0.13 mm, 0.72 ± 0.09 mm, 0.73 ± 0.04 mm, 0.67 ± 0.06 mm, 0.66 ± 0.07 mm, 1.50 ± 0.12 mm, 1.10 ± 0.05 mm, and 0.45 ± 0.04 mm, appropriately. The present case report emphasizes that orthodontic correction of altered tooth position (angulation, inclination, and rotation) under certain clinical conditions may be considered as an effective method for soft tissue contour optimization in cases when pre-treatment tooth position could be interpreted as a causative factor or associated with diagnosed recession. The following outcomes could be related, but not limited to creeping attachment mechanism, bone-housing centering effects, optimization of occlusal load distribution with ruling out peak zones of strain accumulation, and mucogingival stress leveling. Due to the authors' knowledge, the present case report is the first one where the signs of spontaneous recession repair after orthodontic treatment were evidenced with the intraoral scans and quantified by the specifically implemented digital analysis approach.

Gingival shape analysis using surface curvature estimation of the intraoral scans

Background

Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape.

Methods

The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival shape analysis. Six different curvature measures and three different diameters were tested for surface curvature estimation on central (n = 78) and interdental ROI (n = 88) of patients with advanced periodontitis to quantify gingiva with a novel gingival shape parameter (GS). The reproducibility was evaluated by repeating the method on two consecutive intraoral scans obtained with a scan-rescan process of the same patient at the same time point (n = 8).

Results

Minimum and mean curvature measures computed at 2 mm diameter seem optimal GS to quantify shape at central and interdental ROI, respectively. The mean (and standard deviation) of the GS was 0.33 ± 0.07 and 0.19 ± 0.09 for central ROI using minimum, and interdental ROI using mean curvature measure, respectively, computed at a diameter of 2 mm. The method’s reproducibility evaluated on scan-rescan models for the above-mentioned ROI and curvature measures was 0.02 and 0.01, respectively.

Conclusions

Surface curvature estimation of the intraoral optical scans presents a precise and highly reproducible method for the objective gingival shape quantification enabling the detection of subtle changes. A careful selection of parameters for surface curvature estimation and curvature measures is required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02322-y.

Evaluation of gingival recessions with conventional versus digital methods

OBJECTIVES

The present study aimed to compare the conventional clinical and digital methods evaluating differences in gingival recession (ΔREC) in patients with advanced periodontitis treated with the non-surgical treatment protocol.

METHODS

Agreement between the methods was evaluated on a sample of ten patients with periodontitis (stage III/IV, grade B/C) with acquired clinical measurements and digital models from baseline (T0) and 12-months after non-surgical treatment of periodontitis (T1). The evaluation was performed on maxillary teeth from right to left second premolar resulting in overall 99 teeth. Clinical evaluation was performed by subtracting the distance measurements between gingival margin and cemento-enamel junction, obtained at T0 and T1 by a calibrated examiner (intra-examiner agreement >90%). The digital evaluation was performed directly by measuring the distance between the gingival margins on superimposed T0 and T1 digital models. Using Bland-Altman and statistical analysis, all six measurements sites around each included tooth (n=594) acquired with both methods were compared.

RESULTS

Median ΔREC (5th and 95th percentile) acquired with a conventional clinical and digital method was 0.0mm (-2.0 - 1.0) and -0.4mm (-1.6 - 0.8), respectively (p<0.0001). The complete agreement between rounded digital and clinical ΔREC values was only 38%, revealing high disagreement also confirmed by Bland-Altman analysis with 95% limits of agreement ranging from -2.6 to 1.8mm. Absolute differences between the methods higher than 0.5 and 1 mm, was found in 61% and 38% of measurement sites, respectively.

CONCLUSIONS

The conventional clinical method for ΔREC evaluation exhibits lower sensitivity and accuracy than the digital method.

CLINICAL SIGNIFICANCE

The quality of both clinical and research data in periodontology and implantology can be considerably improved by the digital method while still preserving the compatibility with the conventional clinical method.

A novel computer-aided method for direct measurements and visualization of gingival margin changes

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.