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

Knowledge, attitude, and perception of orthodontic students, and orthodontists regarding role of artificial intelligence in field of orthodontics-An online cross-sectional survey

BACKGROUND

Artificial intelligence (AI) is an emerging technology in orthodontics. The objective of this survey was to evaluate the knowledge, attitude, and perception (KAP) of orthodontists and postgraduate students regarding the plausible employment of AI within the realm of orthodontics.

METHODS

An observational, cross-sectional, online questionnaire survey was conducted with 440 participants (264 postgraduates and 176 faculty members). The questionnaire was divided into four domains: Part A, focused on sociodemographic characteristics, Part B (eight questions) identifying the basic knowledge of the participants about the use of AI in the field of orthodontics, Part C (six questions) assessing the participants' perceptions of the use of AI, and Part D (five questions) assessing the attitudes of participants towards AI. The KAP scores of the participants regarding the use of AI in the field of orthodontics were assessed using a three-point Likert scale for 17 questions and two multiple-choice questions. Responses were analyzed using the chi-square test, Kruskal-Wallis test, and Mann-Whitney test.

RESULTS

A total of 266 participants completed the survey, and the majority agreed with the use of AI in the field of orthodontics, particularly for 3-dimensional diagnosis of orthognathic surgeries, cephalometric analysis, and prediction of treatment outcomes. Most participants felt that AI training should be incorporated into the postgraduate curriculum (73%), and were willing to incorporate it into clinical practice (74%). Barriers to the use of AI were high costs, lack of technical knowledge, and lack of awareness. The participants' KAP scores showed a weak negative correlation with age, years of experience, and designation.

CONCLUSION

The present study concluded that most of the participants were optimistic about the future of AI in orthodontics. Although most orthodontists and postgraduate students had knowledge of AI, there were many barriers to its use in the field of orthodontics.

Training, use, and modifications related to artificial intelligence in postgraduate orthodontic programs in North America

INTRODUCTION

The use of artificial intelligence (AI) is growing quickly and has already had a significant impact on the practice of orthodontics. This study aimed to explore the degree to which the study and use of AI have been integrated into accredited postgraduate orthodontic programs in North America.

METHODS

An anonymous electronic survey was sent to each of North America's 74 orthodontic residency programs, requesting information from either the program director or department chair. Data were collected and analyzed using SPSS (version 28; IBM, Armonk, NY) and Excel (Microsoft, Redmond, Wash).

RESULTS

Forty-one valid surveys were analyzed. Among the respondents, 56.1% had implemented or planned to implement AI instruction into their program. Among those who reported using this technology, 60.9% indicated that they have applied these principles for research purposes. Most respondents (87.8%) noted that they have not developed seminars and/or AI training to be included in their curriculum, whereas residents in 17.1% of respondent programs have advocated for these modifications. Lack of expertise and availability in the schedule (71.4%) are common challenges associated with the dearth of curriculum changes. Most programs (68.3%) encouraged their residents to attend continuing education for AI fundamentals, while 75.6% reported that they do not encourage their residents to use AI for patient care, research, or didactic assignments. Several programs (68.3%) reported not updating their academic manuals and syllabi with new AI policies, nor have they installed new AI detection software (80.5%).

CONCLUSIONS

More than half of orthodontic residencies have implemented AI into their program in some capacity. The continual advancements of AI algorithms will require modifications to orthodontic residency programs. AI-related education should be implemented into academic curricula to provide residents with the tools necessary to thrive in an AI-driven practice.

The Accuracy of Algorithms Used by Artificial Intelligence in Cephalometric Points Detection: A Systematic Review

The use of artificial intelligence in orthodontics is emerging as a tool for localizing cephalometric points in two-dimensional X-rays. AI systems are being evaluated for their accuracy and efficiency compared to conventional methods performed by professionals. The main objective of this study is to identify the artificial intelligence algorithms that yield the best results for cephalometric landmark localization, along with their learning system. A literature search was conducted across PubMed-MEDLINE, Cochrane, Scopus, IEEE Xplore, and Web of Science. Observational and experimental studies from 2013 to 2023 assessing the detection of at least 13 cephalometric landmarks in two-dimensional radiographs were included. Studies requiring advanced computer engineering knowledge or involving patients with anomalies, syndromes, or orthodontic appliances, were excluded. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Newcastle-Ottawa Scale (NOS) tools. Of 385 references, 13 studies met the inclusion criteria (1 diagnostic accuracy study and 12 retrospective cohorts). Six were high-risk, and seven were low-risk. Convolutional neural networks (CNN)-based AI algorithms showed point localization accuracy ranging from 64.3 to 97.3%, with a mean error of 1.04 mm ± 0.89 to 3.40 mm ± 1.57, within the clinical range of 2 mm. YOLOv3 demonstrated improvements over its earlier version. CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. Although CNN-based algorithms generate results very quickly and reproducibly, they still do not achieve the accuracy of orthodontists.

Artificial Intelligence and Modern Technology in Dentistry: Attitudes, Knowledge, Use, and Barriers Among Dentists in Croatia-A Survey-Based Study

AIM

This study aims to assess Croatian dentists' knowledge, attitudes, and use of artificial intelligence (AI) and modern technology, while also identifying perceived barriers to AI and modern technology adoption and evaluating the need for further education and training.

MATERIALS AND METHODS

A cross-sectional survey was conducted in February 2024 among general dentists in Croatia using a self-structured questionnaire. A total of 200 respondents filled out the questionnaire. It included five sections: socio-demographic and professional information, self-assessment of AI and modern technology use, knowledge of AI in dentistry, current innovations and devices used in practice, and barriers to AI and modern technology integration in practice. Data were analyzed using descriptive statistics and a regression analysis to explore relationships between socio-demographic factors and AI knowledge.

RESULTS

The mean knowledge of AI systems was 3.62 ± 2.56 out of a possible score of 7, indicating relatively poor knowledge, with 47.5% demonstrating knowledge below the median. Most respondents (76.0%) did not use AI systems and modern technology in practice; however, prosthodontics (13.0%) and oral surgery (10.0%) were identified as the primary fields utilizing these technologies. Respondents rated their knowledge of modern technologies and AI as weak or moderate, with 60.5% engaged in continuous education. Despite 76.0% not using AI daily, 71.0% believed that these technologies could enhance patient care. Participants interested in further training showed significantly better knowledge of AI applications (p = 0.030). Major barriers included acquisition and maintenance costs (59.0%) and financial constraints (58.0%).

CONCLUSIONS

The study revealed that most respondents had poor knowledge of AI systems. Despite this, there is a recognition of AI's and modern technology potential in dentistry, emphasizing the need for enhanced education and training in this field.

How reliable is the artificial intelligence product large language model ChatGPT in orthodontics?

OBJECTIVES

To evaluate the reliability of information produced by the artificial intelligence-based program ChatGPT in terms of accuracy and relevance, as assessed by orthodontists, dental students, and individuals seeking orthodontic treatment.

MATERIALS AND METHODS

Frequently asked and curious questions in four basic areas related to orthodontics were prepared and asked in ChatGPT (Version 4.0), and answers were evaluated by three different groups (senior dental students, individuals seeking orthodontic treatment, orthodontists). Questions asked in these basic areas of orthodontics were about: clear aligners (CA), lingual orthodontics (LO), esthetic braces (EB), and temporomandibular disorders (TMD). The answers were evaluated by the Global Quality Scale (GQS) and Quality Criteria for Consumer Health Information (DISCERN) scale.

RESULTS

The total mean DISCERN score for answers on CA for students was 51.7 ± 9.38, for patients was 57.2 ± 10.73 and, for orthodontists was 47.4 ± 4.78 (P = .001). Comparison of GQS scores for LO among groups: students (3.53 ± 0.78), patients (4.40 ± 0.72), and orthodontists (3.63 ± 0.72) (P < .001). Intergroup comparison of ChatGPT evaluations about TMD was examined in terms of the DISCERN scale, with the highest value given in the patients group (57.83 ± 11.47) and lowest value in the orthodontist group (45.90 ± 11.84). When information quality evaluation about EB was examined, it GQS scores were >3 in all three groups (students: 3.50 ± 0.78; patients: 4.17 ± 0.87; orthodontists: 3.50 ± 0.82).

CONCLUSIONS

ChatGPT has significant potential in terms of usability for patient information and education in the field of orthodontics if it is developed and necessary updates are made.

Large-Language Models in Orthodontics: Assessing Reliability and Validity of ChatGPT in Pretreatment Patient Education

BACKGROUND

Patients seeking orthodontic treatment may use large language models (LLMs) such as Chat-GPT for self-education, thereby impacting their decision-making process. This study assesses the reliability and validity of Chat-GPT prompts aimed at informing patients about orthodontic side effects and examines patients' perceptions of this information.

MATERIALS AND METHODS

To assess reliability, n = 28 individuals were asked to generate information from GPT-3.5 and Generative Pretrained Transformer 4 (GPT-4) about side effects related to orthodontic treatment using both self-formulated and standardized prompts. Three experts evaluated the content generated based on these prompts regarding its validity. We asked a cohort of 46 orthodontic patients about their perceptions after reading an AI-generated information text about orthodontic side effects and compared it with the standard text from the postgraduate orthodontic program at Aarhus University.

RESULTS

Although the GPT-generated answers mentioned several relevant side effects, the replies were diverse. The experts rated the AI-generated content generally as "neither deficient nor satisfactory," with GPT-4 achieving higher scores than GPT-3.5. The patients perceived the GPT-generated information as more useful and more comprehensive and experienced less nervousness when reading the GPT-generated information. Nearly 80% of patients preferred the AI-generated information over the standard text.

CONCLUSIONS

Although patients generally prefer AI-generated information regarding the side effects of orthodontic treatment, the tested prompts fall short of providing thoroughly satisfactory and high-quality education to patients.

Revolutionizing Smiles: Advancing Orthodontics Through Digital Innovation

Orthodontics is undergoing a digital revolution, transforming traditional techniques with modern technology. This evolution is driven by the need for precise diagnosis and treatment planning. Digital platforms, including digital radiography and cone beam computed tomography (CBCT), are replacing conventional methods, enhancing documentation, analysis, and appliance production. Three-dimensional imaging enables customized treatment plans and appliance design using computer-aided design and computer-aided manufacture (CAD/CAM). Integration of digital models and software facilitates treatment simulation and patient communication. Digital videography enhances diagnostic capabilities. Embracing digital processes is essential for improved patient care and practice efficiency in orthodontics. This review article on digital orthodontics aims to provide a comprehensive overview and critical analysis of the current advancements, technologies, applications, benefits, and challenges in the field of orthodontics utilizing digital tools and technologies.

Artificial Intelligence in Orthodontics: Critical Review

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

New Approaches and Technologies in Orthodontics

In recent years, new diagnostic and treatment approaches in orthodontics have arisen, and there is thus a need for researchers and practitioners to stay up to date with these innovations [...].

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients' facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).