A Novel Method for Digital Reconstruction of the Mucogingival Borderline in Optical Scans of Dental Plaster Casts

Colorimetric analysis of intraoral scans: A novel approach for detecting gingival inflammation

BACKGROUND

Gingivitis, a widely prevalent oral health condition, affects up to 80% of the population. Traditional assessment methods for gingivitis rely heavily on subjective clinical evaluation. This study seeks to explore the efficacy of interpreting the color metrics from intraoral scans to objectively differentiate between healthy and inflamed gingiva.

METHODS

This study used the percentage of bleeding on probing (BOP%) as the clinical reference standard. Intraoral scans, obtained before and after gingivitis treatment using a scanner, were analyzed through a custom MATLAB script to quantify HSV (hue, saturation, value) and CIELAB (Commission Internationale de l'Eclairage L*a*b*) color coordinates. The region of interest was a 2-mm-wide gingival strip along the buccal margin of the maxillary anterior teeth. Linear regression analysis was performed to evaluate the relationship between photometric outcomes and continuous, dichotomous, and categorical BOP data. Diagnostic accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), as well as sensitivity and specificity measures.

RESULTS

The analysis included clinical and digital color data from 110 scans, adhering to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. The multilevel linear regression analysis underscored a significant correlation between the BOP% and digital color metrics, specifically the CIELAB a* (red-green chroma), CIELAB b* (yellow-blue chroma), and color saturation, with AUC performances of 70%, 79.5%, and 80.8%, respectively.

CONCLUSION

Digital color analysis of intraoral scans has demonstrated a range of performance from acceptable to excellent in distinguishing sites with BOP. This innovative approach presents a promising tool for dentists and researchers in the accurate diagnosis, screening, and management of gingivitis.

PLAIN LANGUAGE SUMMARY

Our study focuses on finding a better way to detect gingivitis, a common gum disease affecting many people. Traditional methods rely on the dentist's visual inspection, which can be subjective. We explored the use of color measurements from digital intraoral scans to objectively identify healthy versus inflamed gums. We analyzed 110 scans from 55 participants, examining the color differences in the gums before and after treatment. By measuring specific color values, we achieved up to 80.8% accuracy in distinguishing between healthy and inflamed gums. This method could offer a more reliable tool for dentists and researchers to diagnose and manage gingivitis, leading to better oral health outcomes.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

Peri-implant phenotype, calprotectin and MMP-8 levels in cases diagnosed with peri-implant disease

OBJECTIVES

The purpose of this prospective cohort study is to evaluate the effect of peri-implant phenotype (PPh) on the severity of peri-implant diseases and the results of non-surgical mechanical treatment (NSMT), along with calprotectin (CLP) and MMP-8(matrix metalloproteinase-8) levels.

MATERIALS AND METHODS

77 implants from 39 patients were included. The implants were categorized Group-1(peri-implant mucositis), Group-2(peri-implantitis).Baseline (0. Month-PrT) clinical parameters (PD, GI, PI, BOP, CAL) and radiographic bone loss were documented, and peri-implant crevicular fluid (PICF) samples were collected. Various intruments and methodologies were employed to assess PPh components (mucosa thickness, supracrestal tissue height, keratinized mucosa) and peri-implant attached mucosa (AM). NSMT was applied to diseased implant sites. All clinical parameters were reassessed again by taking PICF samples at the 6th month-after treatment (PT). In PICF samples obtained from both groups, MMP-8 and CLP levels were evaluated using the ELISA test.

RESULTS

PrT-PD,PrT-GI,PrT-CAL and PrT-BOP percentage values in Group-2 were significantly higher than Group-1.PrT-PD,PrTPI scores are significantly higher in thin biotype implants. All components of the PPh and AM were significantly lower in thin biotype. Intra-group time-dependent changes of MMP-8 and CLP were significant in both groups (p < 0.05). When the relationship between thin and thick biotype and biochemical parameters was evaluated, the change in PrT-PT didn't show a significant difference (p > 0.05).

CONCLUSIONS

PPh plays a role in influencing the severity of peri-implant diseases. However, the impact of phenotype on NSMT outcomes was similar in both groups.

CLINICAL RELEVANCE

The PPh should be considered when planning implant surgery.

Reconstruction of dental roots for implant planning purposes: a retrospective computational and radiographic assessment of single-implant cases

PURPOSE

The aim of the study was to assess the deviation between clinical implant axes (CIA) determined by a surgeon during preoperative planning and reconstructed tooth axes (RTA) of missing teeth which were automatically computed by a previously introduced anatomical SSM.

METHODS

For this purpose all available planning datasets of single-implant cases of our clinic, which were planned with coDiagnostix Version 9.9 between 2018 and 2021, were collected for retrospective investigation. Informed consent was obtained. First, the intraoral scans of implant patients were annotated and subsequently analyzed using the SSM. The RTA, computed by the SSM, was then projected into the preoperative planning dataset. The amount and direction of spatial deviation between RTA and CIA were then measured.

RESULTS

Thirty-five patients were implemented. The mean distance between the occlusal entry point of anterior and posterior implants and the RTA was 0.99 mm ± 0.78 mm and 1.19 mm ± 0.55, respectively. The mean angular deviation between the CIA of anterior and posterior implants and the RTA was 12.4° ± 3.85° and 5.27° ± 2.97° respectively. The deviations in anterior implant cases were systematic and could be corrected by computing a modified RTA (mRTA) with decreased deviations (0.99 mm ± 0.84 and 4.62° ± 1.95°). The safety distances of implants set along the (m)RTA to neighboring teeth were maintained in 30 of 35 cases.

CONCLUSION

The RTA estimated by the SSM revealed to be a viable implant axis for most of the posterior implant cases. As there are natural differences between the anatomical tooth axis and a desirable implant axis, modifications were necessary to correct the deviations which occurred in anterior implant cases. However, the presented approach is not applicable for clinical use and always requires manual optimization by the planning surgeon.

Reconstruction of dental roots for implant planning purposes: a feasibility study

Purpose

Modern virtual implant planning is a time-consuming procedure, requiring a careful assessment of prosthetic and anatomical factors within a three-dimensional dataset. In order to facilitate the planning process and provide additional information, this study examines a statistical shape model (SSM) to compute the course of dental roots based on a surface scan.

Material and methods

Plaster models of orthognathic patients were scanned and superimposed with three-dimensional data of a cone-beam computer tomography (CBCT). Based on the open-source software “R”, including the packages Morpho, mesheR, Rvcg and RvtkStatismo, an SSM was generated to estimate the tooth axes. The accuracy of the calculated tooth axes was determined using a leave-one-out cross-validation. The deviation of tooth axis prediction in terms of angle or horizontal shift is described with mean and standard deviation. The planning dataset of an implant surgery patient was additionally analyzed using the SSM.

Results

71 datasets were included in this study. The mean angle between the estimated tooth-axis and the actual tooth-axis was 7.5 ± 4.3° in the upper jaw and 6.7 ± 3.8° in the lower jaw. The horizontal deviation between the tooth axis and estimated axis was 1.3 ± 0.8 mm close to the cementoenamel junction, and 0.7 ± 0.5 mm in the apical third of the root. Results for models with one missing tooth did not differ significantly. In the clinical dataset, the SSM could give a reasonable aid for implant positioning.

Conclusions

With the presented SSM, the approximate course of dental roots can be predicted based on a surface scan. There was no difference in predicting the tooth axis of existent or missing teeth. In clinical context, the estimation of tooth axes of missing teeth could serve as a reference for implant positioning. However, a higher number of training data must be achieved to obtain increasing accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-022-02716-x.