Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning

Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages

INTRODUCTION

The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation generated by the artificial intelligence of dentOne software (DIORCO Co, Ltd, Yongin, South Korea) and Medit Ortho Simulation software (Medit Corp, Seoul, South Korea).

METHODS

Twelve maxillary and mandibular pretreatment dental scan sets comprising 286 teeth were collected for this investigation from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University. The scans were imported as standard tessellation language files into both dentOne and Medit Ortho Simulation software. Automatic segmentation was run on each software. The number of successfully segmented teeth vs failed segmentations was recorded to determine the success rate of automated segmentation of each program. Evaluation of success and/or failure was based on the software's identification of the teeth and the quality of the segmentation. The mesiodistal tooth width measurements after segmentation using both tested software programs were compared with those measured on the unsegmented scan using Meshmixer software (Autodesk, San Rafael, Calif). The unsegmented scans served as the reference standard.

RESULTS

A total of 288 teeth were examined. Successful identification rates were 99% and 98.3% for Medit and dentOne, respectively. Success rates of segmenting the lingual surfaces of incisors were significantly higher in Medit than in dentOne (93.7% vs 66.7%, respectively; P <0.001). DentOne overestimated the mesiodistal width of canines (0.11 mm, P = 0.032), premolars (0.22 mm, P < 0.001), and molars (0.14 mm, P = 0.043) compared with the reference standard, whereas Medit overestimated the mesiodistal width of premolars only (0.13 mm, P = 0.006). Bland-Altman plots showed that mesiodistal tooth width agreement limits exceeded 0.2 mm between each software and the reference standard.

CONCLUSIONS

Both artificial intelligence-segmentation software demonstrated acceptable accuracy in tooth segmentation. There is a need for improvement in segmenting incisor lingual tooth surfaces in dentOne. Both software programs tended to overestimate the mesiodistal widths of segmented teeth, particularly the premolars. Artificial intelligence-segmentation needs to be manually adjusted by the operator to ensure accuracy. However, this still does not solve the problem of proximal surface reconstruction by the software.

Deep learning-based tooth segmentation methods in medical imaging: A review

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

Deep learning-based automatic segmentation of bone graft material after maxillary sinus augmentation

OBJECTIVES

To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone-beam computed tomography (CBCT) images.

MATERIALS AND METHODS

One hundred paired CBCT scans (a preoperative scan and a postoperative scan) were collected and randomly allocated to training (n = 82) and testing (n = 18) subsets. The ground truths of graft materials were labeled by three observers together (two experienced surgeons and a computer engineer). A deep learning model including a 3D V-Net and a 3D Attention V-Net was developed. The overall performance of the model was assessed through the testing data set. The comparative accuracy and inference time consumption of the model-driven and manual segmentation (by two surgeons with 3 years of experience in dental implant surgery) were conducted on 10 CBCT scans from the test samples.

RESULTS

The deep learning model had a Dice coefficient (Dice) of 90.36 ± 2.53%, a 95% Hausdorff distance (HD) of 1.59 ± 0.82 mm, and an average surface distance (ASD) of 0.38 ± 0.11 mm. The proposed model only needed 7.2 s, while the surgeon took 19.15 min on average to complete a segmentation task. The overall performances of the model were significantly superior to those of surgeons.

CONCLUSIONS

The proposed deep learning model yielded a more accurate and efficient performance of automatic segmentation of graft material after SA than that of the two surgeons. The proposed model could facilitate a powerful system for volumetric change evaluation, dental implant planning, and digital dentistry.

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.

Three-dimensional evaluation of a virtual setup considering the roots and alveolar bone in molar distalization cases

We aimed to evaluate root parallelism and the dehiscence or fenestrations of virtual teeth setup using roots isolated from cone beam computed tomography (CBCT) images. Sixteen patients undergoing non-extraction orthodontic treatment with molar distalization were selected. Composite teeth were created by merging CBCT-isolated roots with intraoral scan-derived crowns. Three setups were performed sequentially: crown setup considering only the crowns, root setup-1 considering root alignment, and root setup-2 considering the roots and surrounding alveolar bone. We evaluated the parallelism and exposure of the roots and compared the American Board of Orthodontics Objective Grading System (ABO-OGS) scores using three-dimensionally printed models among the setups. The mean angulation differences between adjacent teeth in root setups-1 and -2 were significantly smaller than in the crown setup, except for some posterior teeth (p < 0.05). The amount of root exposure was significantly smaller in root setup-2 compared to crown setup and root setup-1 except when the mean exposure was less than 0.6 mm (p < 0.05). There was no significant difference in ABO-OGS scores among the setups. Thus, virtual setup considering the roots and alveolar bone can improve root parallelism and reduce the risk of root exposure without compromising occlusion quality.

The accuracy of virtual setup in simulating treatment outcomes in orthodontic practice: a systematic review

OBJECTIVES

To evaluate the accuracy of virtual orthodontic setup in simulating treatment outcomes and to determine whether virtual setup should be used in orthodontic practice and education.

MATERIALS AND METHODS

A systematic search was performed in five electronic databases: PubMed, Scopus, Embase, ProQuest Dissertations & Theses Global, and Google Scholar from January 2000 to November 2022 to identify all potentially relevant evidence. The reference lists of identified articles were also screened for relevant literature. The last search was conducted on 30 November 2022.

RESULTS

This systematic review included twenty-one articles, where all of them were assessed as moderate risk of bias. The extracted data were categorized into three groups, which were: (1) Virtual setup and manual setup; (2) Virtual setup and actual outcomes in clear aligner treatment; (3) Virtual setup and actual outcomes in fixed appliance treatment. There appeared to be statistically significant differences between virtual setups and actual treatment outcomes, however the discrepancies were clinically acceptable.

CONCLUSION

This systematic review supports the use of orthodontic virtual setups, and therefore they should be implemented in orthodontic practice and education with clinically acceptable accuracy. However, high-quality research should be required to confirm the accuracy of virtual setups in simulating treatment outcomes.

Artificial Intelligence in Medicine and Dentistry

INTRODUCTION

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

OBJECTIVE

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

CONCLUSION

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

Clinical tooth segmentation based on local enhancement

The tooth arrangements of human beings are challenging to accurately observe when relying on dentists’ naked eyes, especially for dental caries in children, which is difficult to detect. Cone-beam computer tomography (CBCT) is used as an auxiliary method to measure patients’ teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This study proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model, which is more suitable for dental CBCT images, is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder–decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.