Evaluation of root position during orthodontic treatment via multiple intraoral scans with automated registration technology

Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography

BACKGROUND

External root resorption (ERR) during orthodontic treatment is a common concern, and accurate quantification is crucial for assessing outcomes and minimizing long-term complications. This study aims to quantify ERR using automated root extraction from cone beam computed tomography (CBCT), combined with a novel root partitioning method and enhanced through the integration of intraoral scans for improved accuracy.

METHODS

Thirty-six patients with malocclusion were included and divided into two groups. Root extraction was performed on CBCT images using artificial intelligence (AI), Simultaneously, crown data from intraoral scans were integrated to create composite dental models in Geomagic software. Pre- and post-treatment models were aligned based on crowns. A novel partitioning method was then used to analyze root volume changes in three dimensions. Finally, these changes were analyzed according to age, tooth region, and extraction treatment using SPSS software.

RESULTS

A statistically significant reduction in root volume was observed post-treatment in both groups (P < 0.001). Anterior teeth exhibited greater ERR, especially in the upper anterior teeth of Group II (extraction treatment, P < 0.05). Posterior maxillary teeth in Group I showed less ERR (P < 0.05). ERR was more pronounced in the apical third of the root (P < 0.001). Group II experienced greater overall ERR, particularly in the apical third, whereas Group I showed more ERR at the cervical third (P < 0.05).

CONCLUSION

This 3D quantification method provides a novel assessment of ERR, with distribution influenced by age, tooth position, extraction treatment, and root region.

Unveiling the role of artificial intelligence applied to clear aligner therapy: A scoping review

OBJECTIVES

To conduct a scoping review on the application of artificial intelligence (AI) in clear aligner therapy and to assess the extent of AI integration and automation in orthodontic software currently available to orthodontists.

DATA AND SOURCES

A systematic electronic literature search was performed in the following databases: PubMed, Embase, Web of Science, Cochrane Library, and Scopus. Also, grey literature resources up to March 2024 were reviewed. English-language studies on potential AI applications for clear aligner therapy were included based on an independent evaluation by two reviewers. An assessment of the automation steps in orthodontic software available on the market up to March 2024 was also conducted.

STUDY SELECTION AND RESULTS

Out of 708 studies, 41 were included. Sixteen articles focused on tooth segmentation, four on registration of digital models, 13 on digital setup, and eight on remote monitoring. Moreover, 13 aligner software programs were identified and assessed for their level of automation. Only one software demonstrated complete automation of the steps involved in the orthodontic digital workflow.

CONCLUSIONS

None of the 13 identified aligner software programs were evaluated in the 41 included studies. However, AI-based tooth segmentation achieved 98 % accuracy, while AI effectively merged CBCT and IOS data, supported digital measurements, predicted treatment outcomes, and showed potential for remote monitoring.

CLINICAL SIGNIFICANCE

AI applications in clear aligner therapy are on the rise. This scoping review enables orthodontists to identify AI-based solutions in orthodontic planning and understand its implications, which can potentially enhance treatment efficiency, accuracy, and predictability.

Automatic multimodal registration of cone-beam computed tomography and intraoral scans: a systematic review and meta-analysis

OBJECTIVES

To evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in dentistry.

METHODS

A comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and IEEE Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective studies.

RESULTS

Of the 493 articles identified, 22 met the inclusion criteria. Among these, 14 studies used geometry-based methods, 7 used artificial intelligence (AI) techniques, and 1 compared the accuracy of both approaches. Geometry-based methods primarily utilize two-stage coarse-to-fine registration algorithms, which require relatively fewer computational resources. In contrast, AI methods leverage advanced deep learning models, achieving significant improvements in automation and robustness.

CONCLUSIONS

Recent advances in CBCT and IOS registration technologies have considerably increased the efficiency and accuracy of 3D dental modelling, and these technologies show promise for application in orthodontics, implantology, and oral surgery. Geometry-based algorithms deliver reliable performance with low computational demand, whereas AI-driven approaches demonstrate significant potential for achieving fully automated and highly accurate registration. Future research should focus on challenges such as unstable registration landmarks or limited dataset diversity, to ensure their stability in complex clinical scenarios.

Artificial intelligence for orthodontic diagnosis and treatment planning: A scoping review

OBJECTIVES

To provide an overview of artificial intelligence (AI) applications in orthodontic diagnosis and treatment planning, and to evaluate whether AI improves accuracy, reliability, and time efficiency compared to expert-based manual approaches, while highlighting its current limitations.

DATA

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

SOURCES

An electronic search was performed on PubMed, Web of Science, and Embase electronic databases. Additional studies were identified from Google Scholar and by hand searching through included studies. The search was carried out until June 2023 without restriction of language and publication year.

STUDY SELECTION

After applying the selection criteria, 71 articles were included in the review. The main research areas were classified into three domains based on the purpose of AI: diagnostics (n = 29), landmark identification (n = 20) and treatment planning (n = 22).

CONCLUSION

This scoping review shows that AI can be used in various orthodontic diagnosis and treatment planning applications, with anatomical landmark detection being the most studied domain. While AI shows potential in improving time efficiency and reducing operator variability, the accuracy and reliability have not yet consistently surpassed those of expert clinicians. At all moments, human supervision remains essential. Further advances and optimizations are necessary to strive towards automated patient-specific treatment planning.

CLINICAL SIGNIFICANCE

AI in orthodontics has shown its ability to serve as a decision-support system, thereby enhancing the efficiency of diagnostics and treatment planning within orthodontics digital workflow."

The performance of artificial intelligence models in generating responses to general orthodontic questions: ChatGPT vs Google Bard

INTRODUCTION

This study aimed to evaluate and compare the performance of 2 artificial intelligence (AI) models, Chat Generative Pretrained Transformer-3.5 (ChatGPT-3.5; OpenAI, San Francisco, Calif) and Google Bidirectional Encoder Representations from Transformers (Google Bard; Bard Experiment, Google, Mountain View, Calif), in terms of response accuracy, completeness, generation time, and response length when answering general orthodontic questions.

METHODS

A team of orthodontic specialists developed a set of 100 questions in 10 orthodontic domains. One author submitted the questions to both ChatGPT and Google Bard. The AI-generated responses from both models were randomly assigned into 2 forms and sent to 5 blinded and independent assessors. The quality of AI-generated responses was evaluated using a newly developed tool for accuracy of information and completeness. In addition, response generation time and length were recorded.

RESULTS

The accuracy and completeness of responses were high in both AI models. The median accuracy score was 9 (interquartile range [IQR]: 8-9) for ChatGPT and 8 (IQR: 8-9) for Google Bard (Median difference: 1; P <0.001). The median completeness score was similar in both models, with 8 (IQR: 8-9) for ChatGPT and 8 (IQR: 7-9) for Google Bard. The odds of accuracy and completeness were higher by 31% and 23% in ChatGPT than in Google Bard. Google Bard's response generation time was significantly shorter than that of ChatGPT by 10.4 second/question. However, both models were similar in terms of response length generation.

CONCLUSIONS

Both ChatGPT and Google Bard generated responses were rated with a high level of accuracy and completeness to the posed general orthodontic questions. However, acquiring answers was generally faster using the Google Bard model.

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.