Automatic recognition of landmarks on digital dental models

Recent Advances in Intraoral Scanners

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

Evaluating the long-term stability of a predefined palatal region for tooth movement analysis

OBJECTIVES

We conducted a three-dimensional (3D) analysis of orthodontic tooth movement (TM) using digital dental models (DMs), focusing on the palatal region of interest (PROI), aiming to evaluate the long-term stability of the PROI, validate the 3D TM analysis with PROI registration, and compare it with conventional lateral cephalometric analyses.

METHODS

Twenty adult patients treated with a multibracket appliance were evaluated at their first visit (T0) and at least 5 years later (T1) using DMs and lateral cephalograms (LCs). The long-term stability of PROI was assessed by calculating the point cloud distances between DM-T0 and DM-T1. TM analysis using DM with PROI registration for the maxillary central incisors was assessed through linear and angular measurements in the sagittal view and subsequently compared with the LCs.

RESULTS

The average point cloud distance of the PROI between DM-T0 and DM-T1 was 0.21 mm (standard deviation, 0.13 mm). TM analysis using DMs demonstrated excellent reproducibility for both linear and angular measurements (intra-rater correlation coefficient, > 0.99). The 95 % limits of agreement between the DM and LC measurements were < 5.14° for angular change, 3.53 mm for horizontal displacement, and 0.98 mm for vertical displacement. No significant differences were observed in the angular and linear measurements when the TM was compared using the DMs and LCs.

CONCLUSIONS

The PROI remained stable for over 5 years, supporting the reproducibility and accuracy of TM assessment using PROI registration in orthodontic clinical practice.

CLINICAL SIGNIFICANCE

DM analysis lacks the risks associated with X-ray exposure and can be easily performed in daily clinical practice, indicating its potential for future clinical applications. These findings further support the use of DM with PROI registration for TM analysis in orthodontic clinical practice, emphasizing its long-term stability and reproducibility.

Isotopological remeshing and statistical shape analysis: Enhancing premolar tooth wear classification and simulation with machine learning

OBJECTIVE

The aim of this study was to evaluate the accuracy of a combined approach based on an isotopological remeshing and statistical shape analysis (SSA) to capture key anatomical features of altered and intact premolars. Additionally, the study compares the capabilities of four Machine Learning (ML) algorithms in identifying or simulating tooth alterations.

METHODS

113 premolar surfaces from a multicenter database were analyzed. These surfaces were processed using an isotopological remeshing method, followed by a SSA. Mean Euclidean distances between the initial and remeshed STL files were calculated to assess deviation in anatomical landmark positioning. Seven anatomical features were extracted from each tooth, and their correlations with shape modes and morphological characteristics were explored. Four ML algorithms, validated through three-fold cross-validation, were assessed for their ability to classify tooth types and alterations. Additionally, twenty intact teeth were altered and then reconstructed to verify the method's accuracy.

RESULTS

The first five modes encapsulated 76.1% of the total shape variability, with a mean landmark positioning deviation of 10.4 µm (±6.4). Significant correlations were found between shape modes and specific morphological features. The optimal ML algorithms demonstrated high accuracy (>83%) and precision (>86%). Simulations on intact teeth showed discrepancies in anatomical features below 3%.

CONCLUSION

The combination of an isotopological remeshing with SSA showed good reliability in capturing key anatomical features of the tooth.

CLINICAL SIGNIFICANCE

The encouraging performance of ML algorithms suggests a promising direction for supporting practitioners in diagnosing and planning treatments for patients with altered teeth, ultimately improving preventive care.

Analysis of Three-Dimensional Tooth Movement: A Comparative Study Between Digital Dental Models and Craniofacial Models

OBJECTIVE

This study aims to validate the efficacy of using a digital dental model (DM) with reference to the palatal region of interest (PROI) for assessing orthodontic tooth movement (TM) by comparing it with the analysis of a computed tomography (CT) model with reference to the cranial region of interest (CROI).

MATERIALS AND METHODS

Thirty-four patients (mean age: 21 years and 11 months) with jaw deformities underwent DM and CT scans before and after presurgical orthognathic treatment. Linear and angular measurements during TM were conducted in three dimensions using both DM and CT to assess reliability.

RESULTS

DM analysis with PROI registration exhibited high levels of reproducibility, with minimal standard errors in X, Y, and Z displacements (<0.15 mm) and 0.43 degrees in angular change. CT analysis with CROI registration demonstrates similarly high reproducibility, with standard errors inferior to DM analysis (<0.20 mm). Bland-Altman analysis indicated agreement in linear changes of each X, Y, and Z displacement between DM and CT measurements, with limits of agreement (LOA) below 0.91 mm.

CONCLUSIONS

The results of this study suggest that the PROI, focusing on the third palatal rugae and the horizontal part of the palatal vault, serves as a reliable reference region for evaluating three-dimensional (3D) tooth movement.

CLINICAL SIGNIFICANCE

Digital dental models offer distinct advantages including the absence of X-ray exposure, no metal artifacts, and the ability to generate high-resolution 3D models. The methodology demonstrated high precision and reproducibility, supporting its potential clinical utility in orthodontic treatment planning and assessment.

Reliability and time-based efficiency of artificial intelligence-based automatic digital model analysis system

OBJECTIVES

To compare the reliability, reproducibility, and time-based efficiency of automatic digital (AD) and manual digital (MD) model analyses using intraoral scan models.

MATERIAL AND METHODS

Two examiners analysed 26 intraoral scanner records using MD and AD methods for orthodontic modelling. Tooth size reproducibility was confirmed using a Bland-Altman plot. The Wilcoxon signed-rank test was conducted to compare the model analysis parameters (tooth size, sum of 12-teeth, Bolton analysis, arch width, arch perimeter, arch length discrepancy, and overjet/overbite) for each method, including the time taken for model analysis.

RESULTS

The MD group exhibited a relatively larger spread of 95% agreement limits when compared with AD group. The standard deviations of repeated tooth measurements were 0.15 mm (MD group) and 0.08 mm (AD group). The mean difference values of the 12-tooth (1.80-2.38 mm) and arch perimeter (1.42-3.23 mm) for AD group was significantly (P < 0.001) larger than that for the MD group. The arch width, Bolton, and overjet/overbite were clinically insignificant. The overall mean time required for the measurements was 8.62 min and 0.56 min for the MD and AD groups, respectively.

LIMITATIONS

Validation results may vary in different clinical cases because our evaluation was limited to mild-to-moderate crowding in the complete dentition.

CONCLUSIONS

Significant differences were observed between AD and MD groups. The AD method demonstrated reproducible analysis in a considerably reduced timeframe, along with a significant difference in measurements compared to the MD method. Therefore, AD analysis should not be interchanged with MD, and vice versa.

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.

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.