Dentistry,internet,orthodontic elastics,orthodontics,YouTube™

Background: Dental age (DA) estimation, which is widely used in orthodontics, pediatric dentistry, and forensic dentistry, predicts chronological age (CA) by assessing tooth development and maturation. Most methods rely on radiographic evaluation of tooth mineralization and eruption stages to assess DA. With the increasing adoption of large language models (LLMs) in medical sciences, use of ChatGPT has extended to processing visual data. The aim of this study, therefore, was to evaluate the performance of ChatGPT-4 in estimating DA from panoramic radiographs using three conventional methods (Nolla, Haavikko, and London Atlas) and to compare its accuracy against both orthodontist assessments and CA. Methods: In this retrospective study, panoramic radiographs of 511 Turkish children aged 6–17 years were assessed. DA was estimated using the Nolla, Haavikko, and London Atlas methods by both orthodontists and ChatGPT-4. The DA–CA difference and mean absolute error (MAE) were calculated, and statistical comparisons were performed to assess accuracy and sex differences and reach an agreement between the evaluators, with significance set at p < 0.05. Results: The mean CA of the study population was 12.37 ± 2.95 years (boys: 12.39 ± 2.94; girls: 12.35 ± 2.96). Using the London Atlas method, the orthodontists overestimated CA with a DA–CA difference of 0.78 ± 1.26 years (p < 0.001), whereas ChatGPT-4 showed no significant DA–CA difference (0.03 ± 0.93; p = 0.399). Using the Nolla method, the orthodontist showed no significant DA–CA difference (0.03 ± 1.14; p = 0.606), but ChatGPT-4 underestimated CA with a DA–CA difference of −0.40 ± 1.96 years (p < 0.001). Using the Haavikko method, the evaluators underestimated CA (orthodontist: −0.88; ChatGPT-4: −1.18; p < 0.001). The lowest MAE for ChatGPT-4 was obtained when using the London Atlas method (0.59 ± 0.72), followed by Nolla (1.33 ± 1.28) and Haavikko (1.51 ± 1.41). For the orthodontists, the lowest MAE was achieved when using the Nolla method (0.86 ± 0.75). Agreement between the orthodontists and ChatGPT-4 was highest when using the London Atlas method (ICC = 0.944, r = 0.905). Conclusions: ChatGPT-4 showed the highest accuracy with the London Atlas method, with no significant difference from CA for either sex or the lowest prediction error. When using the Nolla and Haavikko methods, both ChatGPT-4 and the orthodontist tended to underestimate age, with higher errors. Overall, ChatGPT-4 performed best when using visually guided methods and was less accurate when using multi-stage scoring methods.

Aim: To evaluate orthodontists’ perspectives on clear aligner treatment in Turkey.Methods: A novel web-based survey consisting of 14 questions was developed and sent to 2027 members of the Turkish Orthodontic Society via e-mail to evaluate the reasons for why or why not they prefer clear aligners, their opinions about different brands in the market and their perspective on the future of this treatment modality. The association between demographic data and multiple choice questions was examined using Kruskal Wallis and Mann-Whitney U tests. The association between Likert-type questions and practice characteristics was analyzed using a one-way analysis of variance (ANOVA) and Turkey’s post hoc tests.Results: 62.7% of the 218 respondents currently treat their patients using clear aligners and 76.7% of them prefer Invisalign (Align Technology, California, USA), a clear aligner brand. The main reasons given by orthodontists to use clear aligners were ‘not to lag behind in technology’ and ‘to have prestige in the community’. The majority of participants (83.8%) treat fewer patients with clear aligners than with fixed appliances. 70% of the surveyed orthodontists do not believe that treatment with clear aligners will completely replace treatments with fixed appliances in the near future. 28.6% of the participants stated that they do not intend to use clear aligner treatment in at least the next 1-2 years due to ‘low financial income’ and ‘complexity of clear aligner treatments.Conclusion: The majority of the surveyed orthodontists currently use clear aligners in their practice, not because they believe clear aligners are more effective or more comfortable than braces or because they are more profitable, but rather to have prestige in the community and not to lag behind in technology. Therefore, it seems that fixed appliance treatment will maintain its place in orthodontic practice as an option for the near future.

Aim: This study evaluated the content, reliability, and quality of videos about early orthodontic treatment with clear aligners on YouTube. Methodology: Using the most frequently employed search terms—“Invisalign First” and “aligner for kids”—the authors searched YouTube for relevant videos and recorded the number of views, likes, comments, video duration, and days since uploading. An 11-point content score was employed to identify low- and high-quality videos, and the global quality scale (GQS) was used to examine the quality of the videos. This study adopted a modified version of the DISCERN tool for evaluating the reliability of health and medical information and used the Mann-Whitney U test to compare the video groups with high- and low-quality content. A p-value of <.05 was deemed statistically significant. Results: A content analysis classified 26% of the YouTube videos as high-quality content and 74% as low-quality content. The most frequent topics in the videos were descriptions of the appliance (66%) and the types and mechanism of the appliance (51%), whereas the least mentioned topics were the cost of treatment (13%), the disadvantages of the appliance (5%), and comparisons of treatment options (15%). The quality of the videos was evaluated as moderate in 36% of the 61 videos, poor or generally poor in 28%, and good or excellent in 36%. The mean reliability score (2.49±1.29) indicates that the videos’ reliability was generally poor. The high-quality content group had a greater mean video length, viewing rate, and GQS and reliability score than the low-quality content group. A statistically significant positive correlation was identified between content score, GQS, and reliability score values (p <.05). Conclusion: YouTube video content relating to early orthodontic treatment with clear aligners was generally inadequate. The quality of the videos was moderate, but the reliability score indicated that they were not generally reliable. Orthodontists should be aware of the inadequacies of YouTube recordings and ensure that patients have access to accurate and reliable recordings.